This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller.
PID Tuning using Ziegler Nicholas - MATLAB ApproachWaleed El-Badry
This is an unreleased lab for undergraduate Mechatronics students to know how to practice Ziegler Nicholas method to find the PID factors using MATLAB.
The document discusses Model Reference Adaptive Systems (MRAS) for adaptive control. It describes the MIT rule, which is the original approach for MRAS. The MIT rule adjusts controller parameters based on the error between system output and reference model output. Several examples are provided to illustrate parameter adaptation for systems with adjustable gain, first order, and second order dynamics using the MIT rule. The normalized MIT rule is also introduced to improve parameter convergence.
Automatic Irrigation System is a prototype for a system of irrigation or watering automatically based on the Arduino microcontroller integrated with proximity sensors (Ultrasonic Sensor), the DC motor and the pump using LED indicator lights.
Made by :
Andika Jamal Nurganda 151611004
Putri Sintia Sari 151611021
Rizki Verdian 151611025
Refrigeration and Air Conditioning Engineering
Polytechnic State of Bandung
2016
This document discusses different types of PID controllers including proportional (P), integral (I), derivative (D), PI, PD, and PID controllers. It provides the transfer functions and describes the basic functionality of each type. Tuning methods for PID controllers are presented, including Ziegler-Nichols tuning when the dynamic plant model is known or unknown. Examples are given to illustrate tuning a PID controller to achieve approximately 25% overshoot for a control system.
"Automatic Intelligent Plant Irrigation System using Arduino and GSM board"Disha Modi
Automatic irrigation is a form of irrigation system that incorporates the theory of control, power of wireless technology and feedback system with irrigation. The aim of our project is not only to minimize this manual intervention by the farmer in farm field, but also to successfully water garden plants planted in pots too. Which is why we are using micro- controller based Automated Irrigation system will serve the following purposes: 1) As there is no un-planned usage of water, a lot of water is saved from being wasted. 2) The irrigation is done only when there is not enough moisture in the soil and the microcontroller decides when should the pump be turned on/off, saves a lot time for the farmers. This also gives much needed rest to the farmers and helps, as they don’t have to go and turn the pump on/off manually. 3)This irrigation system can be monitor by user wirelessly. User can receive notification and can provide proper commands via his cell phone whenever necessary.
The document describes a home appliance control system based on GSM network technology that allows users to remotely control appliances via SMS. A microcontroller receives SMS commands and uses a relay circuit to operate appliances. Key advantages are ubiquitous access, wireless operation for adaptability and cost-effectiveness, and power conservation. Limitations include the receiver needing a constant power supply and cellular signal to operate connected devices.
it is a prototype arduino based auto irrigation system which turns on the pump while the field is dry. it uses soil moisture sensor to detect the amount of soil moisture content. As the system is arduino based it uses an arduino software which can be downloaded from https://www.arduino.cc/en/Main/Software
automatic plant irrigation using aurdino and gsm technologythamil arasan
The document describes an automatic plant irrigation system using Arduino and GSM. It contains the circuit diagram and working of the system. The system monitors soil humidity and temperature using sensors and sends the readings to users via SMS through a GSM modem. Users can remotely control the irrigation pump by sending SMS commands. When power is available but water is not flowing, it alerts users. This automatic irrigation system addresses issues faced by farmers like unpredictable electricity supply and health hazards of manual pesticide spraying.
PID Tuning using Ziegler Nicholas - MATLAB ApproachWaleed El-Badry
This is an unreleased lab for undergraduate Mechatronics students to know how to practice Ziegler Nicholas method to find the PID factors using MATLAB.
The document discusses Model Reference Adaptive Systems (MRAS) for adaptive control. It describes the MIT rule, which is the original approach for MRAS. The MIT rule adjusts controller parameters based on the error between system output and reference model output. Several examples are provided to illustrate parameter adaptation for systems with adjustable gain, first order, and second order dynamics using the MIT rule. The normalized MIT rule is also introduced to improve parameter convergence.
Automatic Irrigation System is a prototype for a system of irrigation or watering automatically based on the Arduino microcontroller integrated with proximity sensors (Ultrasonic Sensor), the DC motor and the pump using LED indicator lights.
Made by :
Andika Jamal Nurganda 151611004
Putri Sintia Sari 151611021
Rizki Verdian 151611025
Refrigeration and Air Conditioning Engineering
Polytechnic State of Bandung
2016
This document discusses different types of PID controllers including proportional (P), integral (I), derivative (D), PI, PD, and PID controllers. It provides the transfer functions and describes the basic functionality of each type. Tuning methods for PID controllers are presented, including Ziegler-Nichols tuning when the dynamic plant model is known or unknown. Examples are given to illustrate tuning a PID controller to achieve approximately 25% overshoot for a control system.
"Automatic Intelligent Plant Irrigation System using Arduino and GSM board"Disha Modi
Automatic irrigation is a form of irrigation system that incorporates the theory of control, power of wireless technology and feedback system with irrigation. The aim of our project is not only to minimize this manual intervention by the farmer in farm field, but also to successfully water garden plants planted in pots too. Which is why we are using micro- controller based Automated Irrigation system will serve the following purposes: 1) As there is no un-planned usage of water, a lot of water is saved from being wasted. 2) The irrigation is done only when there is not enough moisture in the soil and the microcontroller decides when should the pump be turned on/off, saves a lot time for the farmers. This also gives much needed rest to the farmers and helps, as they don’t have to go and turn the pump on/off manually. 3)This irrigation system can be monitor by user wirelessly. User can receive notification and can provide proper commands via his cell phone whenever necessary.
The document describes a home appliance control system based on GSM network technology that allows users to remotely control appliances via SMS. A microcontroller receives SMS commands and uses a relay circuit to operate appliances. Key advantages are ubiquitous access, wireless operation for adaptability and cost-effectiveness, and power conservation. Limitations include the receiver needing a constant power supply and cellular signal to operate connected devices.
it is a prototype arduino based auto irrigation system which turns on the pump while the field is dry. it uses soil moisture sensor to detect the amount of soil moisture content. As the system is arduino based it uses an arduino software which can be downloaded from https://www.arduino.cc/en/Main/Software
automatic plant irrigation using aurdino and gsm technologythamil arasan
The document describes an automatic plant irrigation system using Arduino and GSM. It contains the circuit diagram and working of the system. The system monitors soil humidity and temperature using sensors and sends the readings to users via SMS through a GSM modem. Users can remotely control the irrigation pump by sending SMS commands. When power is available but water is not flowing, it alerts users. This automatic irrigation system addresses issues faced by farmers like unpredictable electricity supply and health hazards of manual pesticide spraying.
SCADA stands for Supervisory Control and Data Acquisition. It refers to a system that collects data from sensors at remote locations and sends it to a central computer for monitoring and control. The central monitoring system communicates with remote terminal units or programmable logic controllers through communication links. SCADA systems allow operators to monitor entire systems in real-time with little human intervention through functions like data acquisition, supervisory control, alarms, logging, and trending.
The document describes a project submitted by 6 students for their Diploma in Mechanical Engineering. It involves the design and construction of a tree climbing robot. The robot uses grippers and movements similar to an ape to climb trees and has a maximum loading capacity of 3 kilograms. The document outlines the construction, working principle, advantages, mechanical and electrical parts details, circuit details, finishing, and cost estimation of the tree climbing robot.
This document describes a smart irrigation system that uses sensors to measure soil moisture, temperature, humidity and water levels. The system has a transmitter section with sensors that sends the sensor readings via Zigbee modules to a receiver section. The receiver section has a microcontroller that receives the data and sends messages to farmers via GSM if irrigation is needed. The system automatically provides water to crops based on sensor readings to save water and reduce human intervention in agriculture.
Pid controller tuning using fuzzy logicRoni Roshni
This document provides an overview of tuning a PID controller with fuzzy logic. It introduces fuzzy logic and discusses how it can be applied to PID tuning. Specifically, it discusses using fuzzy set-point weighting to tune the PID controller by determining the proportional weighting factor b(t) using a fuzzy inference system based on the error e(t) and change in error. It also discusses traditional Ziegler-Nichols tuning and compares the performance of fixed versus fuzzy set-point weighting tuning. The conclusion is that fuzzy logic provides benefits like balancing rise time and overshoot to obtain better performance than traditional methods.
arduino based automtic irrigation systemMiJanurSimon
This document presents an automatic irrigation system controlled by an Arduino. The system includes soil moisture and temperature sensors to monitor conditions and control a water pump via a relay.
The objective is to minimize manual intervention by farmers and prevent over or under irrigation. The block diagram shows the Arduino, sensors, LCD display, relay and water pump.
The circuit diagram and components include an Arduino, sensors, LCD, relay module, solar panel and battery. Working steps explain how the sensors send signals to the Arduino to control the relay and pump based on soil moisture and temperature readings. Results show the system automatically supplies water as needed. The conclusion discusses benefits like conserving water, being low cost and useful for farmers and
This document describes an advanced irrigation system using a soil moisture sensor to conserve water. The system uses a microcontroller and soil moisture sensor to automatically water plants only when needed. It measures the soil moisture level and compares it to a threshold value set by the user. If the soil is dry (below the threshold), a relay is activated to supply water. This removes the need for manual intervention by farmers and helps save up to 60% of irrigation water typically wasted through over-watering. The system guide includes the code and wiring diagram to build the automated irrigation system.
This document describes an automatic plant watering system that uses sensors like soil moisture, humidity, light, and ultrasonic sensors along with an Arduino board to control water pumps, sprinklers, lights, and fans without human intervention. The system works by sensing soil moisture and humidity levels and turning on water pumps when levels drop below a threshold. It also controls lights and fans based on light and humidity readings. An LCD display shows sensor values and system status while a GSM module sends status messages to users. The automatic system aims to efficiently water plants and save water compared to manual methods.
This document presents information on various automation topics including variable frequency drives (VFDs), programmable logic controllers (PLCs), and supervisory control and data acquisition (SCADA) systems. It discusses the working and programming of VFDs and provides advantages of automation, VFDs, and PLCs. It describes the major components of PLCs and examples of PLC programming software. It also provides information on SCADA systems, including how they work in control rooms and examples of SCADA software. The conclusion discusses how automation can improve efficiency, accuracy, documentation, security, and customer service.
The document is a third year project report submitted by Mr. Jordan King on smart antennas. It investigates adaptive beamforming arrays, which are the key technology behind smart antennas. It studies and simulates popular adaptive algorithms like Least Mean Squares (LMS), Recursive Least Squares (RLS), Constant Modulus (CMA) and Conjugate Gradient Method (CGM). It compares the performance of these algorithms in terms of accuracy and convergence rate. The report focuses on adaptive beamforming as the main capability that enables smart antennas to be smart.
https://technoelectronics44.blogspot.com/
AUTOMATIC WATER DISPENSER full document, it very helps for everyone who studies engineering as ELECTRONIC, STUDENTS. it has a circuit diagram and code for easily implement
The "Automatic Reservoir Monitoring and Control System", called ARMAC for short, is a computerized control system for the monitoring and control of dam gates and reservoirs. Today, ARMAC is successfully applied in numerous dams all over the world. The ARMAC system measures the water level of a dam every 15 minutes. The system takes into consideration the geographical contours of each reservoir, the so-called area volume curves, as well as decades of historical river-flow data and the outfall water levels. The real-time data are compared with the stored data and the current local requirements. This way the optimum opening of the spillway gates is calculated. Rising and sinking water levels are measured by means of water level sensors installed in upstream and downstream positions. The ARMAC software allows the exact monitoring and adjustment of the water flows to maintain specified water levels.
Automatic Irrigation System Project ReportEr Gupta
The project implements an automatic irrigation system using soil moisture sensors. Sensors in each agricultural field detect the humidity level in the soil and send signals to a microcontroller. If a field's soil becomes dry, the sensor sends a signal to the microcontroller which then supplies water to that field until the soil moisture level increases again. The system aims to reduce water usage through automated irrigation only when needed.
The document describes a microcontroller-based automated drip irrigation system. It contains sensors to monitor soil moisture and temperature, which send signals to a microcontroller. The microcontroller controls valves to supply water through drip lines as needed based on the sensor readings, maintaining optimal soil conditions. This system saves water compared to manual irrigation and allows for more precise control and record keeping than traditional methods.
This system uses sensor technology with the microcontroller, relay, DC motor and battery. Behave as an intelligent switching system that detects the soil moisture level and irrigates the plant if necessary. The ON / OFF motor will automatically be based on the dryness level of the soil.
ppt submit by Prashant D. Auti
Mobile based electricity billing system (mo bebis)Vijeth Ds
The document describes a mobile-based electricity billing system (MoBEBIS) developed to address issues with the manual billing process. MoBEBIS consists of mobile apps for meter readers and customers, as well as a website for administrative functions. It utilizes image processing and neural networks to extract meter readings from photos. This automated process eliminates human errors and provides customers with updated billing information and the ability to track usage. The system aims to improve customer service and the efficiency of the electricity board's operations.
This document describes an Arduino-based automatic watering plant system that uses sensors to more simply and conveniently irrigate plants. The system uses a moisture sensor to detect soil dryness, a temperature sensor to monitor temperature, and an LDR sensor to measure light intensity. It displays sensor readings on an LCD and automatically controls a fan, water pump, and LED lights based on the sensor values to irrigate plants as needed without manual labor. The system aims to help farmers more easily irrigate fields and gardens.
1. This book is a compilation of 26 microcontroller-based projects that were featured in Electronics For You magazine between 2001-2009.
2. The book includes projects based on various 8-bit microcontrollers like AT89C51, AT89C2051, AT89C52, AT89S8252, Atmega16, Atmega8535, PIC16F84, and provides details on hardware, software, and PCB layout.
3. A CD accompanying the book contains datasheets, source codes, tutorials, and other files for the projects to help readers implement the projects easily. The book is intended to introduce readers to practical microcontroller-based projects.
Fuzzy logic was used to improve induction motor control by developing alternative control methods to field oriented control. A fuzzy flux controller was developed using two fuzzy logic blocks, one to describe the nonlinear relationship between slip frequency and current, and another fuzzy PI controller for the outer control loop. Simulation results showed the fuzzy flux controller had almost as good performance as field oriented control, but required less development time. The fuzzy flux controller was further improved by replacing the linear PI controller with a nonlinear fuzzy PI controller, achieving even better dynamic performance while maintaining robustness.
1. The document discusses different types of controllers used in industrial processes including proportional (P), integral (I), derivative (D), PI, PID, and their basic concepts and circuit diagrams.
2. Analog controllers use operational amplifiers as building blocks and provide advantages like compact size, fast response, high reliability and accuracy.
3. PID controllers combine proportional, integral and derivative actions to provide fast response, zero steady-state error and ensure stable system operation, though their optimization can be complex. PID controllers are commonly used for temperature and pressure control.
Controller Tuning for Integrator Plus Delay Processes.theijes
A design method for PID controllers based on internal model control (IMC) principles, direct synthesis method (DS), stability analysis (SA) method for pure integrating process with time delay is proposed. Analytical expressions for PID controllers are derived for several common types of process models, including first order and second-order plus time delay models and an integrator plus time delay model. Here in this paper, a simple controller design rule and tuning procedure for unstable processes with delay time is discussed. Simulation examples are included to show the effectiveness of the proposed method
This document discusses optimizing the parameters of fractional order PID (FOPID) controllers for a permanent magnet synchronous motor (PMSM) drive system using a hybrid grey wolf optimizer (HGWO). The PMSM drive utilizes indirect field-oriented control (IFOC) with both conventional PID and FOPID controllers for speed and current control. HGWO is used to determine optimal parameters for both controller types to minimize speed error and current/torque ripples. Simulation results show the FOPID controller achieves faster response and less ripple compared to the PID controller.
SCADA stands for Supervisory Control and Data Acquisition. It refers to a system that collects data from sensors at remote locations and sends it to a central computer for monitoring and control. The central monitoring system communicates with remote terminal units or programmable logic controllers through communication links. SCADA systems allow operators to monitor entire systems in real-time with little human intervention through functions like data acquisition, supervisory control, alarms, logging, and trending.
The document describes a project submitted by 6 students for their Diploma in Mechanical Engineering. It involves the design and construction of a tree climbing robot. The robot uses grippers and movements similar to an ape to climb trees and has a maximum loading capacity of 3 kilograms. The document outlines the construction, working principle, advantages, mechanical and electrical parts details, circuit details, finishing, and cost estimation of the tree climbing robot.
This document describes a smart irrigation system that uses sensors to measure soil moisture, temperature, humidity and water levels. The system has a transmitter section with sensors that sends the sensor readings via Zigbee modules to a receiver section. The receiver section has a microcontroller that receives the data and sends messages to farmers via GSM if irrigation is needed. The system automatically provides water to crops based on sensor readings to save water and reduce human intervention in agriculture.
Pid controller tuning using fuzzy logicRoni Roshni
This document provides an overview of tuning a PID controller with fuzzy logic. It introduces fuzzy logic and discusses how it can be applied to PID tuning. Specifically, it discusses using fuzzy set-point weighting to tune the PID controller by determining the proportional weighting factor b(t) using a fuzzy inference system based on the error e(t) and change in error. It also discusses traditional Ziegler-Nichols tuning and compares the performance of fixed versus fuzzy set-point weighting tuning. The conclusion is that fuzzy logic provides benefits like balancing rise time and overshoot to obtain better performance than traditional methods.
arduino based automtic irrigation systemMiJanurSimon
This document presents an automatic irrigation system controlled by an Arduino. The system includes soil moisture and temperature sensors to monitor conditions and control a water pump via a relay.
The objective is to minimize manual intervention by farmers and prevent over or under irrigation. The block diagram shows the Arduino, sensors, LCD display, relay and water pump.
The circuit diagram and components include an Arduino, sensors, LCD, relay module, solar panel and battery. Working steps explain how the sensors send signals to the Arduino to control the relay and pump based on soil moisture and temperature readings. Results show the system automatically supplies water as needed. The conclusion discusses benefits like conserving water, being low cost and useful for farmers and
This document describes an advanced irrigation system using a soil moisture sensor to conserve water. The system uses a microcontroller and soil moisture sensor to automatically water plants only when needed. It measures the soil moisture level and compares it to a threshold value set by the user. If the soil is dry (below the threshold), a relay is activated to supply water. This removes the need for manual intervention by farmers and helps save up to 60% of irrigation water typically wasted through over-watering. The system guide includes the code and wiring diagram to build the automated irrigation system.
This document describes an automatic plant watering system that uses sensors like soil moisture, humidity, light, and ultrasonic sensors along with an Arduino board to control water pumps, sprinklers, lights, and fans without human intervention. The system works by sensing soil moisture and humidity levels and turning on water pumps when levels drop below a threshold. It also controls lights and fans based on light and humidity readings. An LCD display shows sensor values and system status while a GSM module sends status messages to users. The automatic system aims to efficiently water plants and save water compared to manual methods.
This document presents information on various automation topics including variable frequency drives (VFDs), programmable logic controllers (PLCs), and supervisory control and data acquisition (SCADA) systems. It discusses the working and programming of VFDs and provides advantages of automation, VFDs, and PLCs. It describes the major components of PLCs and examples of PLC programming software. It also provides information on SCADA systems, including how they work in control rooms and examples of SCADA software. The conclusion discusses how automation can improve efficiency, accuracy, documentation, security, and customer service.
The document is a third year project report submitted by Mr. Jordan King on smart antennas. It investigates adaptive beamforming arrays, which are the key technology behind smart antennas. It studies and simulates popular adaptive algorithms like Least Mean Squares (LMS), Recursive Least Squares (RLS), Constant Modulus (CMA) and Conjugate Gradient Method (CGM). It compares the performance of these algorithms in terms of accuracy and convergence rate. The report focuses on adaptive beamforming as the main capability that enables smart antennas to be smart.
https://technoelectronics44.blogspot.com/
AUTOMATIC WATER DISPENSER full document, it very helps for everyone who studies engineering as ELECTRONIC, STUDENTS. it has a circuit diagram and code for easily implement
The "Automatic Reservoir Monitoring and Control System", called ARMAC for short, is a computerized control system for the monitoring and control of dam gates and reservoirs. Today, ARMAC is successfully applied in numerous dams all over the world. The ARMAC system measures the water level of a dam every 15 minutes. The system takes into consideration the geographical contours of each reservoir, the so-called area volume curves, as well as decades of historical river-flow data and the outfall water levels. The real-time data are compared with the stored data and the current local requirements. This way the optimum opening of the spillway gates is calculated. Rising and sinking water levels are measured by means of water level sensors installed in upstream and downstream positions. The ARMAC software allows the exact monitoring and adjustment of the water flows to maintain specified water levels.
Automatic Irrigation System Project ReportEr Gupta
The project implements an automatic irrigation system using soil moisture sensors. Sensors in each agricultural field detect the humidity level in the soil and send signals to a microcontroller. If a field's soil becomes dry, the sensor sends a signal to the microcontroller which then supplies water to that field until the soil moisture level increases again. The system aims to reduce water usage through automated irrigation only when needed.
The document describes a microcontroller-based automated drip irrigation system. It contains sensors to monitor soil moisture and temperature, which send signals to a microcontroller. The microcontroller controls valves to supply water through drip lines as needed based on the sensor readings, maintaining optimal soil conditions. This system saves water compared to manual irrigation and allows for more precise control and record keeping than traditional methods.
This system uses sensor technology with the microcontroller, relay, DC motor and battery. Behave as an intelligent switching system that detects the soil moisture level and irrigates the plant if necessary. The ON / OFF motor will automatically be based on the dryness level of the soil.
ppt submit by Prashant D. Auti
Mobile based electricity billing system (mo bebis)Vijeth Ds
The document describes a mobile-based electricity billing system (MoBEBIS) developed to address issues with the manual billing process. MoBEBIS consists of mobile apps for meter readers and customers, as well as a website for administrative functions. It utilizes image processing and neural networks to extract meter readings from photos. This automated process eliminates human errors and provides customers with updated billing information and the ability to track usage. The system aims to improve customer service and the efficiency of the electricity board's operations.
This document describes an Arduino-based automatic watering plant system that uses sensors to more simply and conveniently irrigate plants. The system uses a moisture sensor to detect soil dryness, a temperature sensor to monitor temperature, and an LDR sensor to measure light intensity. It displays sensor readings on an LCD and automatically controls a fan, water pump, and LED lights based on the sensor values to irrigate plants as needed without manual labor. The system aims to help farmers more easily irrigate fields and gardens.
1. This book is a compilation of 26 microcontroller-based projects that were featured in Electronics For You magazine between 2001-2009.
2. The book includes projects based on various 8-bit microcontrollers like AT89C51, AT89C2051, AT89C52, AT89S8252, Atmega16, Atmega8535, PIC16F84, and provides details on hardware, software, and PCB layout.
3. A CD accompanying the book contains datasheets, source codes, tutorials, and other files for the projects to help readers implement the projects easily. The book is intended to introduce readers to practical microcontroller-based projects.
Fuzzy logic was used to improve induction motor control by developing alternative control methods to field oriented control. A fuzzy flux controller was developed using two fuzzy logic blocks, one to describe the nonlinear relationship between slip frequency and current, and another fuzzy PI controller for the outer control loop. Simulation results showed the fuzzy flux controller had almost as good performance as field oriented control, but required less development time. The fuzzy flux controller was further improved by replacing the linear PI controller with a nonlinear fuzzy PI controller, achieving even better dynamic performance while maintaining robustness.
1. The document discusses different types of controllers used in industrial processes including proportional (P), integral (I), derivative (D), PI, PID, and their basic concepts and circuit diagrams.
2. Analog controllers use operational amplifiers as building blocks and provide advantages like compact size, fast response, high reliability and accuracy.
3. PID controllers combine proportional, integral and derivative actions to provide fast response, zero steady-state error and ensure stable system operation, though their optimization can be complex. PID controllers are commonly used for temperature and pressure control.
Controller Tuning for Integrator Plus Delay Processes.theijes
A design method for PID controllers based on internal model control (IMC) principles, direct synthesis method (DS), stability analysis (SA) method for pure integrating process with time delay is proposed. Analytical expressions for PID controllers are derived for several common types of process models, including first order and second-order plus time delay models and an integrator plus time delay model. Here in this paper, a simple controller design rule and tuning procedure for unstable processes with delay time is discussed. Simulation examples are included to show the effectiveness of the proposed method
This document discusses optimizing the parameters of fractional order PID (FOPID) controllers for a permanent magnet synchronous motor (PMSM) drive system using a hybrid grey wolf optimizer (HGWO). The PMSM drive utilizes indirect field-oriented control (IFOC) with both conventional PID and FOPID controllers for speed and current control. HGWO is used to determine optimal parameters for both controller types to minimize speed error and current/torque ripples. Simulation results show the FOPID controller achieves faster response and less ripple compared to the PID controller.
Enhanced self-regulation nonlinear PID for industrial pneumatic actuatorIJECEIAES
The present article describes the improvement of Self-regulation Nonlinear PID (SN-PID) controller. A new function is introduced to improve the system performance in terms of transient without affecting the steady state performance. It is used to optimize the nonlinear function available on this controller. The signal error is reprocessed through this function, and the result is used to tune the nonlinear function of the controller. Furthermore, the presence of the dead zone on the proportional valve is solved using Dead Zone Compensator (DZC). Simulations and experiments were carried out on the pneumatic positioning system. Comparisons between the existing methods were examined and successfully demonstrated.
IRJET- Optimum Design of PSO based Tuning using PID Controller for an Automat...IRJET Journal
This document discusses using particle swarm optimization (PSO) to tune the parameters of a PID controller for an automatic voltage regulator (AVR) system. The goal is to improve the system's transient performance by minimizing overshoot and steady state error. PSO is applied to optimize the proportional, integral, and derivative parameters of the PID controller. Simulation results show the PSO-tuned PID controller provides significantly better control of the AVR system compared to a traditionally tuned PID controller. The tuned controller gives faster response, lower overshoot, and improved stability when subjected to disturbances and parameter variations.
Optimised control using Proportional-Integral-Derivative controller tuned usi...IJECEIAES
Time delays are generally unavoidable in the designing frameworks for mechanical and electrical systems and so on. In both continuous and discrete schemes, the existence of delay creates undesirable impacts on the underthought which forces exacting constraints on attainable execution. The presence of delay confounds the design structure procedure also. It makes continuous systems boundless dimensional and also extends the readings in discrete systems fundamentally. As the Proportional-IntegralDerivative (PID) controller based on internal model control is essential and strong to address the vulnerabilities and aggravations of the model. But for an real industry process, they are less susceptible to noise than the PID controller.It results in just one tuning parameter which is the time constant of the closed-loop system λ, the internal model control filter factor. It additionally gives a decent answer for the procedure with huge time delays. The design of the PID controller based on the internal model control, with approximation of time delay using Pade’ and Taylor’s series is depicted in this paper. The first order filter used in the design provides good set-point tracking along with disturbance rejection.
This document discusses using particle swarm optimization (PSO) to tune the parameters of proportional-integral-derivative (PID) and fractional-order PID (FOPID) controllers for speed control of a DC motor. PSO is a bio-inspired optimization technique that can automatically tune controller parameters online. The document presents the mathematical model of a DC motor and compares tuning PID and FOPID controllers with PSO. PSO is shown to find optimal controller parameters that improve the dynamic and static performance of the closed-loop speed control system.
DESIGN OF PID CONTROLLERS INTEGRATOR SYSTEM WITH TIME DELAY AND DOUBLE INTEGR...ijics
In this paper first we investigate optimal PID control of a double integrating plus delay process and compare with the SIMC rules. What makes the double integrating process special is that derivative action is actually necessary for stabilization. In control, there is generally a trade-off between performance and
robustness, so there does not exist a single optimal controller. However, for a given robustness level (here defined in terms of the Ms-value) we can find the optimal controller which minimizes the performance J (here defined as the integrated absolute error (IAE)-value for disturbances). Interestingly, the SIMC PID controller is almost identical to the optimal pid controller. This can be seen by comparing the paretooptimal
curve for J as a function of Ms, with the curve found by varying the SIMC tuning parameter Tc.
Second, design of Proportional Integral and Derivative (PID) controllers based on internal model control (IMC) principles, direct synthesis method (DS), stability analysis (SA) method for pure integrating process with time delay is proposed. The performances of the proposed controllers are compared with the
controllers designed by recently reported methods. The robustness of the proposed controllers for the uncertainty in model parameters is evaluated considering one parameter at a time using Kharitonov’s theorem. The proposed controllers are applied to various transfer function models and to non linear model of isothermal continuous copolymerization of styrene-acrylonitrile in CSTR. An experimental set up of tank
with the outlet connected to a pump is considered for implementation of the PID controllers designed by
the three proposed methods to show the effectiveness of the methods.
DESIGN OF PID CONTROLLERS INTEGRATOR SYSTEM WITH TIME DELAY AND DOUBLE INTEGR...ijcisjournal
This document discusses PID controller design methods for integrating processes with time delay. It proposes designing PID controllers using three methods: internal model control (IMC), direct synthesis, and stability analysis. It evaluates the performance of controllers designed with these methods by applying them to models of various chemical processes that can be represented by integrating processes with time delay. These include level control systems, distillation columns, and continuous stirred-tank reactors. The robustness of the proposed controllers is analyzed considering parameter uncertainty using Kharitonov's theorem. Simulation results show the proposed IMC method provides superior performance over other existing tuning methods. An experimental setup is used to validate the effectiveness of the proposed design methods.
A simple nonlinear PD controller for integrating processesISA Interchange
Many industrial processes are found to be integrating in nature, for which widely used Ziegler–Nichols tuned PID controllers usually fail to provide satisfactory performance due to excessive overshoot with large settling time. Although, IMC (Internal Model Control) based PID controllers are capable to reduce the overshoot, but little improvement is found in the load disturbance response. Here, we propose an auto-tuning proportional-derivative controller (APD) where a nonlinear gain updating factor α continuously adjusts the proportional and derivative gains to achieve an overall improved performance during set point change as well as load disturbance. The value of α is obtained by a simple relation based on the instantaneous values of normalized error (eN) and change of error (ΔeN) of the controlled variable. Performance of the proposed nonlinear PD controller (APD) is tested and compared with other PD and PID tuning rules for pure integrating plus delay (IPD) and first-order integrating plus delay (FOIPD) processes. Effectiveness of the proposed scheme is verified on a laboratory scale servo position control system.
This document summarizes a research paper that investigates using genetic algorithms (GA) and differential evolution (DE) to tune PID controller parameters. It first provides background on PID controllers and challenges with traditional tuning methods for complex systems. It then describes how GA and DE can be applied as evolutionary algorithms to search for optimal PID parameters. The performance of GA and DE for tuning high-order, time-delayed, and non-minimum phase systems is evaluated and compared to Ziegler-Nichols tuning.
Experimental evaluation of control performance of MPC as a regulatory controllerISA Interchange
Proportional integral derivative (PID) control is widely practiced as the base layer controller in the industry due to its robustness and design simplicity. However, a supervisory control layer over the base layer, namely a model predictive controller (MPC), is becoming increasingly popular with the advent of computer process control. The use of a supervisory layer has led to different control structures. In this study, we perform an objective investigation of several commonly used control structures such as “Cascaded PI controller,” “DMC cascaded to PI” and “Direct DMC.” Performance of these control structures are compared on a pilot-scale continuous stirred tank heater (CSTH) system. We used dynamic matrix control (DMC) algorithm as a representative of MPC. In the DMC cascaded to PI structure, the flow-loops are regulated by the PI controller. On top of that a DMC manipulates the set-points of the flow-loops to control the temperature and the level of water in the tank. The “Direct DMC” structure, as its name suggests, uses DMC to manipulate the valves directly. Performance of all control structures were evaluated based on the integrated squared error (ISE) values. In this empirical study, the “Direct DMC” structure showed a promise to act as regulatory controller. The selection of control frequency is critical for this structure. The effect of control frequency on controller performance of the “Direct DMC” structure was also studied.
Closed-loop step response for tuning PID fractional-order filter controllersISA Interchange
Analytical methods are usually applied for tuning fractional controllers. The present paper proposes an empirical method for tuning a new type of fractional controller known as PID-Fractional-Order-Filter (FOF-PID). Indeed, the setpoint overshoot method, initially introduced by Shamsuzzoha and Skogestad, has been adapted for tuning FOF-PID controller. Based on simulations for a range of first order with time delay processes, correlations have been derived to obtain PID-FOF controller parameters similar to those obtained by the Internal Model Control (IMC) tuning rule. The setpoint overshoot method requires only one closed-loop step response experiment using a proportional controller (P-controller). To highlight the potential of this method, simulation results have been compared with those obtained with the IMC method as well as other pertinent techniques. Various case studies have also been considered. The comparison has revealed that the proposed tuning method performs as good as the IMC. Moreover, it might offer a number of advantages over the IMC tuning rule. For instance, the parameters of the frac- tional controller are directly obtained from the setpoint closed-loop response data without the need of any model of the plant to be controlled.
DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...IOSR Journals
This document discusses controlling the position of a DC motor using fuzzy proportional-derivative controllers with different defuzzification methods. It first introduces Shravan Kumar Yadav and his background. It then models a DC motor in Simulink and designs a crisp PD controller as a benchmark. Different fuzzy PD controllers using various defuzzification methods are implemented and their responses compared. The fuzzy controllers are able to reject disturbances without retuning, unlike the crisp PD controller. The purpose is to control DC motor position using fuzzy logic control in MATLAB and compare its performance to PID control.
DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...IOSR Journals
This document discusses using fuzzy proportional-derivative (FPD) controllers to control the position of a DC motor. It first describes modeling the DC motor in Simulink and designing a crisp proportional-derivative (PD) controller as a benchmark. Then it discusses designing an FPD controller and comparing the system responses using different defuzzification methods. It finds that the FPD controller is able to reject disturbances without further tuning, unlike the crisp PD controller.
This document discusses using fuzzy proportional-derivative (FPD) controllers to control the position of a DC motor. It first describes modeling the DC motor in Simulink and designing a crisp proportional-derivative (PD) controller as a benchmark. Then it discusses designing an FPD controller and comparing the system responses using different defuzzification methods. It finds that the FPD controller is able to reject disturbances without further tuning, unlike the crisp PD controller.
This document compares the performance of PID, PI, and MPC controllers for controlling water level in a tank process. It describes modeling the first-order plus dead time process in MATLAB and tuning the PID controller using Ziegler-Nichols method. Simulation results show that the MPC controller achieved better performance than the PID and PI controllers in terms of rise time, settling time, and overshoot. Specifically, the MPC controller had the shortest rise time and settling time, as well as the lowest overshoot of the three controllers evaluated.
This document summarizes a study that evaluated the performance of tuned PID controllers for speed control of a DC motor. The researchers developed linear and nonlinear mathematical models of a DC motor and represented the system using state space equations. They then simulated four different PID controllers using MATLAB/Simulink to control the motor speed in response to a step input signal. The system responses under each controller were analyzed and discussed in terms of their performance.
Adaptive proportional integral derivative deep feedforward network for quadr...IJECEIAES
When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hidden layer deep feedforward network (DFN), where the one-step secant algorithm is chosen for initializing the DFN parameters. All the design steps of the proposed adaptive controller are described. The multidimensional particle swarm optimization (PSO) algorithm is used for tuning the DFN parameters. Then, using two simulation efficiency tests, a comparison between the proposed PSO-based APID-DFN, the (non-optimized) APID-DFN, the feedforward network APID, and the fixed-parameter PID controllers proves much efficiency of the proposed adaptive controller. The results illustrate that the proposed PSO-based APID-DFN controller can ensure good quadrotor system stabilization and achieve minimum overshoot and faster convergence speed for all quadrotor motions. Thus, the proposed control strategy could be considered an additional intelligent method-based adaptive control for unmanned aerial vehicles.
This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).
IRJET- Performance Analysis of ACO based PID Controller in AVR System: A ...IRJET Journal
This document provides a review of using Ant Colony Optimization (ACO) technique to tune the parameters of a PID controller for an Automatic Voltage Regulator (AVR) system. ACO is inspired by how real ants find the shortest path between their nest and food sources. It has been applied to determine optimal proportional, integral and derivative gains for the PID controller in order to improve the dynamic performance of the AVR system and maintain the generator terminal voltage following disturbances. The document discusses ACO-PID controller design and compares results to traditional PID tuning through MATLAB simulation. A literature review is also presented covering previous research that has applied optimization techniques like ACO, PSO and GA to tune PID controllers for AVR systems.
Similar to Design of a model reference adaptive PID control algorithm for a tank system (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
Flow Through Pipe: the analysis of fluid flow within pipesIndrajeet sahu
Flow Through Pipe: This topic covers the analysis of fluid flow within pipes, focusing on laminar and turbulent flow regimes, continuity equation, Bernoulli's equation, Darcy-Weisbach equation, head loss due to friction, and minor losses from fittings and bends. Understanding these principles is crucial for efficient pipe system design and analysis.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
e qqqqqqqqqqeuwiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiqw dddddddddd cccccccccccccccv s w c r
cdf cb bicbsad ishd d qwkbdwiur e wetwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww w
dddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddfffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffw
uuuuhhhhhhhhhhhhhhhhhhhhhhhhe qiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii iqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc ccccccccccccccccccccccccccccccccccc bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbu uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuum
m
m mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm m i
g i dijsd sjdnsjd ndjajsdnnsa adjdnawddddddddddddd uw
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
Design of a model reference adaptive PID control algorithm for a tank system
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 1, February 2021, pp. 300~318
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i1.pp300-318 300
Journal homepage: http://ijece.iaescore.com
Design of a model reference adaptive PID control algorithm
for a tank system
Yohan Darcy Mfoumboulou
Departement of Electrical, Electronic, and Computer Engineering,
Cape Peninsula University of Technology, South Africa
Article Info ABSTRACT
Article history:
Received Mar 31, 2020
Revised Jun 20, 2020
Accepted Jul 6, 2020
This paper describes the design of an adaptive controller based on model
reference adaptive PID control (MRAPIDC) to stabilize a two-tank process
when large variations of parameters and external disturbances affect
the closed-loop system. To achieve that, an innovative structure of
the adaptive PID controller is defined, an additional PI is designed to make
sure that the reference model produces stable output signals and three
adaptive gains are included to guarantee stability and robustness of
the closed-loop system. Then, the performance of the model reference
adaptive PID controller on the behaviour of the closed-loop system is
compared to a PI controller designed on MATLAB when both
closed-loop systems are under various conditions. The results demonstrate
that the MRAPIDC performs significantly better than the conventional
PI controller.
Keywords:
Adaptive
Linearization
MIT
MRAPIDC
Nonlinear
Parameters
Stability
This is an open access article under the CC BY-SA license.
Corresponding Author:
Yohan Darcy Mfoumboulou,
Departement of Electrical, Electronic and Computer Engineering,
Cape Peninsula University of Technology,
Bellville Campus, P.O Box 7530, South Africa.
Email: fabolous86yo@yahoo.fr
1. INTRODUCTION
Adaptive control of uncertain processes has become more and more important in industry. Adaptive
controllers differ from ordinary ones, because their parameters are variable, and there is a mechanism for
adjusting these parameters online based on signals in the system [1]. The design of an adaptive PI controller
to stabilize a mass damper-spring system under parameters’ uncertainties was proposed in [2]. The designed
adaptive PI controller adjusts to parameters’ variations, and the output of the process follows the set points,
regardless of the values of the parameters. But it does not guarantee stability when external disturbances and
large variations of parameters occur.
In [3], the design of a PID controller on MATLAB to maintain the level of liquid constant in
a coupled-tank system (CTS) was proposed. The control parameters were found using the trial and error
methodology and the results were analysed in MATLAB/Simulink environments. Proportional (P),
proportional integral (PI), proportional derivative (PD) and proportional integral derivative (PID) controllers
were applied on the process and their performances were compared to select the most suitable control
solution. The PID controller showed superior results, but it did not guarantee stability to disturbances and
variations of plant parameters.
Adaptive controllers, as opposed to conventional constant gain controllers (PID controllers),
are very effective in handling situations where the variations of parameters and environmental changes are
very frequent with the application of model reference adaptive control scheme in a first order system [4].
2. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
301
They noticed that the newer adaptive control schemes could not cope with drastic changes in loads, inertias
and forces, unpredictable and sudden faults, or frequent, or unforeseen disturbances. Most conventional PID
controllers with constant gain were also unable to cope with these problems. For this reason, the authors
developed a control technique to solve these problems and added an adaptation gain to show the effects on
the system performance.
An adaptive control algorithm of a water tank model was simulated in [5]. It was concluded that,
compared to the one degree of freedom (1DOF) algorithm, the two degrees of freedom algorithm (2DOF)
reduced the control input demands, which was important from the practical point of view. But the 2DOF had
slower output response compared to the 1DOF.
A robust optimal adaptive control strategy was developed in [6] to deal with tracking problem of
a quadrotor unmanned aerial vehicle (UAV). The controller has a prominent ability to stabilize nonlinear
dynamic system of quadrotor, force the states to follow desired reference signals, and find optimal solution
for the tracking problem without control input saturation. The performance analysis of a conventional PID
controller and a MRAC was done in [7]. Cylindrical tank interacting and noninteracting systems were
selected as processes to be controlled. The results showed that the MRAC has better overshoot, settling time
and set-point tracking performance than the conventional PID controller.
The development of direct and indirect adaptive control methods to control the power in a TRIGA
MARK II reactor was proposed in [8]. The analysis showed that the adaptive algorithm offers overall better
results than the feedback control algorithm. The adaptive algorithm reduced the settling time up to 25% of
the nominal settling time.
A model reference adaptive controller without integral (MRACWI) parameter for position control of
a DC Motor was designed in [9]. The controller produced better performance in terms of settling time,
percentage overshoot and mean square error as compared with PID controller, standard MRAC and MRAC
with a sigma modification. A drawback of this algorithm is that its performance to variations of parameters
and external disturbances is unknown.
A comparison of the time specification performance between a conventional PID controller and
a modern sliding mode controller (SMC) for a nonlinear inverted pendulum system was done in [10].
The performances of both control strategies were assessed to see which one had better handling of
pendulum’s angle and cart’s position. The overall results of the analysis showed that the sliding mode
controller had faster rising time, better settling time and a much better percentage of overshoot compared to
the conventional PID. Both controllers did not have any steady state errors. Since the inverted pendulum is
a highly nonlinear system, this research showed two drawbacks. The authors did not investigate
the performance of the controllers when external disturbances and variations of parameters occur. These
studies would have made the investigation more realistic.
Advanced PID are also used in the medical sector, [11] proposed a fractional order PID controller
and an integer order PID controller for supressing epileptic activities. Both controllers showed great results to
stabilize the patient, but the fractional order PID controller is more suitable for implementation in FPGA
because it uses less flip-flops. Unfortunately, the study did not take in consideration sudden abnormal
activities of the brain cells to evaluate the time response taken by the controller to stabilize the patient.
This study is crucial to bring the patient back to a good health condition in the shortest time possible.
In [12], a novel data-driven sigmoid-based PI controller was designed to track the angular velocity
of dc motor powered by a dc/dc buck converter. The results of the investigations showed that the data-driven
sigmoid-based PI, which is tuned using global simultaneous perturbation stochastic approximation, yields
a better angular velocity tracking as compared to conventional PI and PI-Fuzzy. A drawback of this study is
that, the performance of the sigmoid-based controller was not evaluated for disturbance rejection.
In [13], the performance of the fractional order proportional-integral-derivative (FOPID) controllers
designed by using artificial bee colony (ABC) for fractional orders systems is compared to conventional PID
controller optimized by the ABC colony algorithm. The results of the simulations showed that the FOPID
controllers had significantly better performance compared to the conventional PID controllers. Unfortunately,
there was no study made to evaluate the performance of the controller when disturbances occur.
An adaptive safe experimentation dynamics (ASED) for data driven neuroendocrine-PID control of
MIMO Systems was designed in [14]. The performance of the ASED based method was compared to
the standard safe experimentation dynamics (SED) and simultaneous perturbation stochastic approximation
(SPSA) based methods. The results of the simulations showed that the ASED and SED based methods have
successfully solved the unstable convergence issue in the existing neuroendocrine-PID based standard SPSA.
Moreover, the presented ASED based algorithm outperforms the SED and the SPSA based methods in
the perspective of the control performance accuracy in terms of lower objective function, total norm error and
total norm input. A drawback of this research is that, the authors did not perform plant’s parameters changes
to see the influence of the adaptive gain of the ASED controller. The research gap and merit of the adaptive
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
302
PID controller developed in this paper compared to the other advanced PID controllers reviewed is that,
the proposed controller can stabilize the closed-loop system when variations of parameters and sudden
random disturbances occur simultaneously. None of the reviewed papers explored this scenario.
The main contribution of this paper is that it presents the design of a model reference adaptive PID
controller (MRAPIDC) based on the MIT approach to stabilize and optimize the linearized model of the two-
tank liquid level process affected by sudden changes of parameters and input disturbance. Then, a reference
model is developed based on control theory, and a PI regulator is designed on MATLAB to control
the reference model. The designed PI controller is a novel idea to add stability to the output signals of
the reference model, hence making the adaptive algorithm more robust. Another novelty of this research is
the inclusion of three new adaptive gains in the final structure of the MRAPIDC to make the closed-loop
system robust when large variations of parameters and sudden external disturbances occur simultaneously.
The controller keeps the percentage of overshoot of the closed-loop system below 10% and its recovery time
to sudden variations of parameters is lower than 5 seconds, which is a huge advantage compared to the other
adaptive controllers reviewed. Another advantage of this adaptive algorithm is that real-time tuning of
the three adaptive gains can be done to improve the performance of the closed-loop system. Furthermore,
the performance of the MRAPIDC is compared to a classic PI controller designed on MATLAB for
the linearized model of the two-tank system. The adaptive control algorithms, PI control algorithm and
the models of the closed-loop systems are simulated in MATLAB/Simulink.
The outline of this paper is as follows: the modelling and simulation of the process is proposed in
section 2. Section 3 discusses the design of a MRAPIDC based on model reference theory. The simulation
results are shown in section 4. Section 5 draws the conclusion.
2. MODELING AND SIMULATION OF THE TWO-TANK SYSTEM
In this paper, a two-tank liquid level system is selected as a plant to be controlled, because it is
a nonlinear inherently unstable system. The system is made of two-tank mounted above a reservoir, which
has the function of a storage element for liquid. The system has an independent pump to pump liquid from
the reservoir to the tanks. The two-tanks are interacting, which means that the liquid moves from one tank to
the other. When two tanks are state dependent, the interaction of liquid between the tanks exhibits a nonlinear
behaviour [15]. The simplified block diagram of the process is shown in Figure 1.
Figure 1. Block diagram of a two-tank liquid level process
The parameters of the two-tank liquid level system are the following:
ℎ1 = level of liquid in tank 1 in 𝑐𝑚
ℎ2 = level of liquid in tank 2 in 𝑐𝑚
𝐴1 = cross sectional area of tank 1 in 𝑐𝑚2
4. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
303
𝐴2 = cross sectional area of tank 2 in 𝑐𝑚2
𝑎1 = cross sectional area of the outlet pipe in tank 1 in 𝑐𝑚2
𝑎2 = cross sectional area of the outlet pipe in tank 2 in 𝑐𝑚2
𝑄𝑖𝑛 = flow rate of liquid into tank 1 𝑐𝑚2
/𝑠𝑒𝑐
𝑄𝑜𝑢𝑡 = flow rate of liquid out of tank 2 𝑐𝑚2
/𝑠𝑒𝑐
𝛽1 = valve ratio of outlet pipe of tank 1
𝛽2 = valve ratio of the outlet pipe of tank 2
g = gravitational force
k = pump gain
u (t) = input voltage to the pump
The nonlinear equations of the two-tank liquid system model can be derived by applying
the Bernoulli’s law of conservation of mass [15]:
(1)
The nonlinear dynamic equations derived from tank 1 are:
(2)
(input flow)
(3)
The dynamic equations derived from tank 2 are:
(4)
(5)
At equilibrium for a continuous liquid level set-point, the derivative of the liquid levels in the tanks
must be zero ( ). In the scenario when: ; the system is state decoupled. Therefore,
to satisfy the conditions of the simulation of the liquid level system: , the level of liquid in tank 1
must be bigger than this of tank 2.
The state space representation of the nonlinear system is the following:
(6)
(7)
Then:
)
(
0
)
(
2
)
(
2
)
(
2
)
(
)
(
1
2
2
2
2
1
2
1
1
1
1
1
1
2
1
t
u
A
k
t
gh
A
a
t
h
g
A
a
t
h
g
A
a
dt
t
dh
dt
t
dh
(8)
out
in Q
Q
dt
dh
A
)
(
2
)
(
1
1
1
1
1 t
gh
a
Q
dt
t
dh
A in
in
Q
t
u
)
(
)
(
2
)
(
[
1
)
(
1
1
1
1
1
t
gh
a
t
ku
A
dt
t
dh
)
(
2
)
(
2
)
(
2
2
2
1
1
1
2
2 t
gh
a
t
gh
a
dt
t
dh
A
)
(
2
)
(
2
)
(
2
2
2
2
1
2
1
1
2
t
gh
A
a
t
gh
A
a
dt
t
dh
0
2
.
1
.
h
h 2
1 h
h
2
1 h
h
)
(
2
)
(
2
1
)
(
)
(
1
)
(
)
(
2
2
2
1
1
1
2
1
1
1
1
2
1
t
gh
a
t
gh
a
A
t
h
a
t
ku
A
dt
t
dh
dt
t
dh
T
T
h
h
h
h
C
y 2
1
2
1 1
0
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
304
The parameters of the two-tank process are given in Table 1.
Table 1. Values of the parameters of the process
Parameters Values
75
1.5
1.5315195
0.6820043
981
30.0024
The next section is to derive a linearized model of the nonlinear tank process to find an accurate equivalence
of both models.
2.1. Linearization of the nonlinear model of the two-tank process
The linearization of the two-tank liquid system is performed around its operating points, and to
achieve that, only the linear terms of the Taylor series expansion of the nonlinear model are considered.
In (7) and (8) are considered to linearize the nonlinear model of the system.
Let define the state variables of the system:
ℎ1 = 𝑥1 = level of liquid in tank 1.
ℎ2 = 𝑥2 = level of liquid in tank 2.
By substituting ℎ1 and ℎ2, by 𝑥1 and 𝑥2 in (7) and (8), it is obtained:
[
𝑥
.
1
𝑥
.
2
] = [
−
𝛽1𝑎1
𝐴1
√2𝑔𝑥1
𝛽1𝑎1
𝐴2
√2𝑔𝑥1 −
𝛽2𝑎2
𝐴2
√2𝑔𝑥2
] + [
𝑘
𝐴1
0
] 𝑢 (9)
𝑦 = 𝐶[𝑥1 𝑥2]𝑇
= [0 1][𝑥1 𝑥2]𝑇
(10)
The (9) and (10) can be expressed by the standard nonlinear model:
𝑥
.
= 𝑓(𝑥) + 𝑔(𝑥)𝑢 = [
𝑓1(𝑥)
𝑓2(𝑥)
] + [
𝑔1(𝑥)
𝑔2(𝑥)
] 𝑢 (11)
𝑦 = 𝑜(𝑥) (12)
where: 𝑓(𝑥), 𝑔(𝑥) and 𝑜(𝑥) are the nonlinear vector functions of the state vector.
The linearization of the nonlinear model is performed according to Taylor series method.
The linearized model is derived based of the nonlinear functions 𝑓1 ,𝑓2, 𝑔1 and 𝑔2 . For the case of the two-
tank system, the nonlinear functions are:
𝑓1 = −
𝛽1𝑎1
𝐴1
√2𝑔𝑥1, 𝑔1 =
𝑘
𝐴1
𝑢 (13)
𝑓2 =
𝛽1𝑎1
𝐴2
√2𝑔𝑥1 −
𝛽2𝑎2
𝐴2
√2𝑔𝑥2, 𝑔2 = 0 (14)
The derivatives of the first function 𝑓1 according to the two states 𝑥1, 𝑥2 and the control input 𝑢:
𝜕
𝜕𝑥1
(𝑓1) =
𝜕
𝜕𝑥1
[−
𝛽1𝑎1
𝐴1
√2𝑔𝑥1]
T
T
h
h
h
h
C
y 2
1
2
1 1
0
)
(
, 2
2
1 cm
A
A
)
(
, 2
1
2 cm
a
a
1
2
)
sec
( 2
cm
g
k
6. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
305
𝜕
𝜕𝑥1
(𝑓1) = −
𝑔𝛽1𝑎1
𝐴1√2𝑔𝑥1
(15)
Then:
𝜕
𝜕𝑥2
(𝑓1) =
𝜕
𝜕𝑥2
[−
𝛽1𝑎1
𝐴1
√2𝑔𝑥1]
𝜕
𝜕𝑥2
(𝑓1) = 0 (16)
The derivative according to 𝑢 is:
𝜕
𝜕𝑢
(𝑓1) = 0 (17)
Derivatives of the second function 𝑓2 according to the two states are:
𝜕
𝜕𝑥1
(𝑓2) =
𝜕
𝜕𝑥1
[
𝛽1𝑎1
𝐴2
√2𝑔𝑥1]
𝜕
𝜕𝑥1
(𝑓2) =
𝑔𝛽1𝑎1
𝐴2√2𝑔𝑥1
(18)
𝜕
𝜕𝑥2
(𝑓2) =
𝜕
𝜕𝑥2
[
𝛽1𝑎1
𝐴2
√2𝑔𝑥1 −
𝛽2𝑎2
𝐴2
√2𝑔𝑥2]
𝜕
𝜕𝑥2
(𝑓2) = −
𝑔𝛽2𝑎2
𝐴2√2𝑔𝑥2
(19)
The terms of the control matrix B can also be found with the same procedure:
Derivative of 𝑔1 per the two states:
𝜕
𝜕𝑥1
(𝑔1) =
𝜕
𝜕𝑥1
[
𝑘
𝐴1
]
𝜕
𝜕𝑥1
(𝑔1) = 0 (20)
For 𝑥2:
𝜕
𝜕𝑥2
(𝑔1) =
𝜕
𝜕𝑥2
[
𝑘
𝐴1
]
𝜕
𝜕𝑥2
(𝑔1) = 0 (21)
Derivative of 𝑔2 according to the two states:
𝜕
𝜕𝑥1
(𝑔2) = 0 (22)
𝜕
𝜕𝑥2
(𝑔2) = 0 (23)
After all the linearized expressions of the system are done, the linearized state space representation
of the two-tank process is:
[
𝑥
.
1
𝑥
.
2
] = [
−
𝑔𝛽1𝑎1
𝐴1√2𝑔𝑥1
0
𝑔𝛽1𝑎1
𝐴2√2𝑔𝑥1
(−
𝑔𝛽2𝑎2
𝐴2√2𝑔𝑥2
)
] [
𝑥1
𝑥2
] + [
𝑘
𝐴1
0
] 𝑢 (24)
𝑦 = [0 1] [
𝑥1
𝑥2
]
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
306
where the coefficients of the matrices defined in (24) are calculated for the equilibrium values of the state
variables given by (6) and (7). At equilibrium for continuous liquid level set-point, the derivative must be
zero (ℎ1
̇ = ℎ2
̇ = 0). In the scenario when: ℎ1 = ℎ2, the system is state decoupled. Therefore, to satisfy
the conditions of the simulation of the liquid level system: ℎ1 > ℎ2. The model defined by (6) and (7) will be
used to develop the adaptive control algorithm.
3. DESIGN OF A MODEL-REFERENCE ADAPTIVE PID-CONTROLLER
The Massachusetts Institute of Technology (MIT) rule is a gradient rule. It was derived at MIT in its
instrumentation laboratory, hence its name. The MIT rule is the original approach to model reference
adaptive control (MRAC) [15, 16]. To give a representation of the MIT rule, let consider 𝜃 as an adjustable
parameter of a controller. The desired closed-loop response of the output is 𝑦𝑚; and the error between
the output 𝑦 of the closed-loop system and the output 𝑦𝑚 of the refrerence model is ε. In the paper,
the desired output 𝑦𝑚 is proposed to be determined by a reference model output. To define the MIT rule,
let consider the following loss function [17]:
𝐽(𝜃) =
1
2
𝜀2
(25)
It is necessary to determine at every moment of time the parameters of the controller in such a way that the
function 𝐽(𝜃) is minimized.
The MIT fundamental approach consists of adjusting the parameters of the closed-loop system such
that the loss function described in (25) is minimized. To minimize the function 𝐽(𝜃), a realistic approach
would be to change the parameters of the system in the direction of the negative gradient of 𝐽:
𝑑𝜃
𝑑𝑡
= −𝛾
𝜕𝐽
𝜕𝜃
= −𝛾𝜀 (
𝜕𝜀
𝜕𝜃
) (26)
where: 𝛾 is an adaptation gain; and (
𝜕𝜀
𝜕𝜃
) is the sensitivity derivative function of the system towards its time-
varying parameters. 𝜃 represents in this case, the time varying parameters of the controller.
In (26) is the MIT rule. The sensitivity derivative expresses how the adjustable parameters influence
the error. In general, it is assumed that the parameter changes are slower than the other variables of
the system. Hence, the sensitivity derivative (
𝜕𝜀
𝜕𝜃
) can be evaluated by assuming that the adjustable parameter
𝜃 is constant. To summarize, the following steps can be used to design an adaptive controller based on
the MIT rule:
Define the coefficients of the transfer function of a plant with unknown parameters.
Choose a reference model.
Choose a control algorithm to achieve perfect model tracking.
Define the error of the closed-loop system.
Derive the expressions of the control parameters.
Apply the negative gradient of 𝐽 to find the updating parameters.
3.1. Determination of the transfer function coefficients for the linearized model
The state space representation of the tank process is represented as given in (24):
[
𝑥
.
1
𝑥
.
2
] = [
−
𝑔𝛽1𝑎1
𝐴1√2𝑔𝑥1
0
𝑔𝛽1𝑎1
𝐴2√2𝑔𝑥1
(−
𝑔𝛽2𝑎2
𝐴2√2𝑔𝑥2
)
] [
𝑥1
𝑥2
] + [
𝑘
𝐴1
0
] 𝑢
𝑦 = [0 1] [
𝑥1
𝑥2
]
To make mathematical calculations much simpler, (24) is rewritten as:
[
𝑥̇1
𝑥̇2
] = [
𝑇11 0
𝑇21 𝑇22
] [
𝑥1
𝑥2
] + [
𝑘
𝐴1
0
] 𝑢
𝑦 = [0 1] [
𝑥1
𝑥2
]
8. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
307
where:
𝑇11 = −
𝑔𝛽1𝑎1
𝐴1√2𝑔𝑥1
; 𝑇21 =
𝑔𝛽1𝑎1
𝐴2√2𝑔𝑥1
; 𝑇22 = −
𝑔𝛽2𝑎2
𝐴2√2𝑔𝑥2
The matrices of the state space model can be represented as:
𝐴 = [
𝑇11 0
𝑇21 𝑇22
] ; 𝐵 = [
𝑘
𝐴1
0
]; 𝐶 = [0 1]; and 𝐷 = 0.
To transform the state space model of the linearized system to transfer function, the following
formula is applied [18]:
𝑇𝑓(𝑠) =
𝑁(𝑠)
𝑃(𝑠)
= 𝐶(𝑠𝐼 − 𝐴)−1
𝐵 + 𝐷
where I is the identity matrix, and:
𝑠𝐼 = [
𝑠 0
0 𝑠
]
The calculation of the transfer function of the linearized model is the following:
𝑇𝑓(𝑠) = [0 1] × ([
𝑠 0
0 𝑠
] − [
𝑇11 0
𝑇21 𝑇22
])
−1
× [
𝑘
𝐴1
0
]
𝑇𝑓(𝑠) = [0 1] × ([
(𝑠 − 𝑇11) 0
−𝑇21 (𝑠 − 𝑇22)
])
−1
× [
𝑘
𝐴1
0
]
Then:
𝑇𝑓(𝑠) = [0 1] × [
𝑎𝑑𝑗 [
(𝑠 − 𝑇11) 0
−𝑇21 (𝑠 − 𝑇22
]
|
(𝑠 − 𝑇11) 0
−𝑇21 (𝑠 − 𝑇22)
|
] × [
𝑘
𝐴1
0
]
𝑇𝑓(𝑠) = [0 1] ×
[
[
(𝑠 − 𝑇22) 0
𝑇21 (𝑠 − 𝑇11)
]
[𝑠2 + (−𝑇11 − 𝑇22)𝑠 + 𝑇11𝑇22]
]
× [
𝑘
𝐴1
0
]
𝑇𝑓(𝑠) = [0 1] ×
[
[
(𝑠 − 𝑇22) × (
𝑘
𝐴1
)
𝑇21 (
𝑘
𝐴1
)
]
[𝑠2 + (−𝑇11 − 𝑇22)𝑠 + 𝑇11𝑇22]
]
𝑇𝑓(𝑠) = [
(𝑜) × (𝑠 − 𝑇22) × (
𝑘
𝐴1
) + (1) × 𝑇21 (
𝑘
𝐴1
)
[𝑠2 + (−𝑇11 − 𝑇22)𝑠 + 𝑇11𝑇22]
]
The final representation of the transfer function of the linearized model is:
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
308
𝑇𝑓(𝑠) =
𝑇21 (
𝑘
𝐴1
)
𝑠2 + (−𝑇11 − 𝑇22)𝑠 + 𝑇11𝑇22
The design of the model reference adaptive PID controller to stabilize the developed linearized model is
described in the next section.
3.2. Procedure to design the model-reference adaptive PID-controller (MRAPIDC) for the linearized
model of the two-tank liquid level system
To make the derivation of the adaptive PID controller much simpler mathematically, the final
expression of the transfer function of the linearized model is simplified as:
𝐺(𝑠) =
𝑎3
𝑎0𝑠2 + 𝑎1𝑠 + 𝑎2
where:
𝑇𝑓(𝑠) = 𝐺(𝑠); 𝑎0 = 1; 𝑎1 = (−𝑇11 − 𝑇22); 𝑎2 = 𝑇11𝑇22 𝑎3 = 𝑇21 (
𝑘
𝐴1
)
The parameters of the new transfer function are defined as:
𝑎1 = 𝜃1𝑙1, 𝑎2 = 𝜃2𝑙2 and 𝑎3 = 𝜃3𝑙3.
𝜃1, 𝜃2 and 𝜃3 are the varying parameters; and 𝑙1, 𝑙2 and 𝑙3 are the fixed parameters of the process.
3.2.1. Design of the desired linear reference model
The linearized model of the tank system is of a second order. Therefore, the linear reference model
can be designed as a typical second order transfer function as follows:
From the design specifications, the values of the dominant poles can be obtained as follows:
𝑅(𝑠) =
𝜔𝑛
2
𝑠2+2𝜔𝑛𝜁𝑠+𝜔𝑛
2 =
𝑌𝑚(𝑠)
𝑅𝑖𝑛(𝑠)
(27)
where: 𝑅(𝑠) is the transfer function of the reference model, 𝑌𝑚 is the output of the reference model and 𝑅𝑖𝑛 is
its input.
Then the output of the reference model is:
𝑌𝑚(𝑠) =
𝜔𝑛
2
𝑠2+2𝜁𝜔𝑛𝑠+𝜔𝑛
2 𝑅𝑖𝑛(𝑠) (28)
To guarantee the stability of the closed-loop system, the reference model must meet the following
design characteristics:
Percentage of Overshoot (PO): 8%
Settling time: 2 seconds
T ime delay: 0 second
Steady state error: 0
From the design specifications, the values of the dominant poles can be obtained as follows [19, 20]:
The damping ratio for a percentage of overshoot (PO) of 8% is:
𝜁 = √
(𝑙𝑛
𝑃𝑂
100%
)2
𝜋2+(𝑙𝑛
𝑃𝑂
100%
)2
𝜁 = √
𝑙𝑛( 0.08)2
𝜋2 + 𝑙𝑛( 0.08)2
= 0.63
10. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
309
The phase angle of the dominant poles is:
𝜑 = 𝑐𝑜𝑠−1
( 𝜁) = 𝑐𝑜𝑠−1
( 0.63)
𝜑 = 50.95∘
The values of the real and imaginary poles are calculated as follow:
𝑅𝑒 𝑎 𝑙(𝑠) = −
4
𝑇𝑠
= −
4
2𝑠
= −2
𝐼𝑚 𝑎 𝑔(𝑠) = 𝑅𝑒 𝑎 𝑙(𝑠) × 𝑡𝑎𝑛( 𝜑) = −2 𝑡𝑎𝑛( 50.95∘
)
𝐼𝑚 𝑎 𝑔(𝑠) = −2.46𝑖
The value of the first dominant pole is:
𝑃1 = 𝑅𝑒 𝑎 𝑙(𝑠) + 𝐼𝑚 𝑎 𝑔(𝑠)
𝑃1 = −2 − 2.46𝑖
The natural frequency of the reference model is found based on the polynomial of the dominant poles:
𝑃𝑑𝑜𝑚(𝑠) = (𝑠 + 2 + 2.46𝑖)(𝑠 + 2 − 2.46𝑖)
𝑃𝑑𝑜𝑚(𝑠) = 𝑠2
+ 4𝑠 + 10.0516
𝜔𝑛 = √10.0516 = 3.17𝑟𝑎𝑑/𝑠
The desired closed-loop transfer function of the reference model is:
𝑅(𝑠) =
10.0516
𝑠2 + 4𝑠 + 10.0516
To ensure stability and robustness of the closed-loop system when large variations of parameters
occur, a proportional integral (PI) controller is designed on MATLAB for the reference model R(s) using
the programming command line 𝑝𝑖𝑑 (𝐾𝑝, 𝐾𝑖) [21, 22]. The parameters 𝐾𝑝 and 𝐾𝑖 are tunned to find their
optimal values. This additional PI has also the ability to guarantee stability when sudden external
disturbances affecting the two-tank process occur. The parameters of the PI controller are:
𝐾𝑝 = 0.001 and 𝐾𝑖 = 4
The transfer function of the PI controller is the following:
𝐺𝑃𝐼(𝑠) =
𝐾𝑝𝑠 + 𝐾𝑖
𝑠
=
0.001𝑠 + 4
𝑠
3.2.2. Selection of the control algorithm to achieve perfect model tracking
To achieve perfect model tracking, the following proportional integral derivative (PID) control
algorithm is selected for the process as:
𝑢(𝑡) = 𝑘𝑝𝑒(𝑡) + 𝑘𝑖 ∫ 𝑒(𝑡)𝑑𝑡 − 𝑘𝑑 𝑦̇(𝑡) (29)
where: 𝑘𝑝 is the proportional gain; 𝑘𝑖 is the integral gain; 𝑘𝑑 is the derivative gain; 𝑦 is the plant output;
and 𝑒(𝑡) = 𝑟𝑖𝑛(𝑡) − 𝑦(𝑡), with 𝑟𝑖𝑛(𝑡) as the input of the reference model.
The representation of the PID controller in Laplace domain is:
𝑈(𝑠) = 𝐾𝑝𝐸(𝑠) +
1
𝑠
𝐾𝑖𝐸(𝑠) − 𝑠𝐾𝑑𝑌(𝑠) (30)
11. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
310
The output 𝑌(𝑠) of the two-tank liquid level process can be found as follows:
𝑌(𝑠) = 𝐺(𝑠)𝑈(𝑠) (31)
The substitution of (30) into (31) gives:
𝑌(𝑠) = 𝐺(𝑠) [𝐾𝑝𝐸(𝑠) +
1
𝑠
𝐾𝑖𝐸(𝑠) − 𝑠𝐾𝑑𝑌(𝑠)]
The closed-loop system is:
𝑌(𝑠) = 𝐺(𝑠)𝐸(𝑠) [𝐾𝑝 +
1
𝑠
𝐾𝑖] − 𝑠𝐾𝑑𝑌(𝑠)𝐺(𝑠) (32)
It is known that:
𝐸(𝑠) = 𝑅𝑖𝑛(𝑠) − 𝑌(𝑠)
Then 𝐸(𝑠) is substituted in (32):
𝑌(𝑠) = 𝐺(𝑠)[𝑅𝑖𝑛(𝑠) − 𝑌(𝑠)] [𝐾𝑝 +
1
𝑠
𝐾𝑖] − 𝑠𝐾𝑑𝑌(𝑠)𝐺(𝑠)
𝑌(𝑠) + [𝐾𝑝 +
1
𝑠
𝐾𝑖 + 𝑠𝐾𝑑] 𝐺(𝑠)𝑌(𝑠) = 𝐺(𝑠)𝑅𝑖𝑛(𝑠) [𝐾𝑝 +
1
𝑠
𝐾𝑖]
𝑌(𝑠) [1 + (𝐾𝑝 +
1
𝑠
𝐾𝑖 + 𝑠𝐾𝑑)𝐺(𝑠)] = 𝐺(𝑠)𝑅𝑖𝑛(𝑠) [𝐾𝑝 +
1
𝑠
𝐾𝑖]
𝑌(𝑠) =
[𝑠𝐾𝑝𝐺(𝑠)+𝐾𝑖𝐺(𝑠)]𝑅𝑖𝑛(𝑠)
𝑠2𝐾𝑑𝐺(𝑠)+(1+𝐾𝑝𝐺(𝑠))𝑠+𝐾𝑖𝐺(𝑠)
(33)
The PID coefficients are not known. It is necessary to determine them in such a way that the PID
controller has adaptive behaviour towards the parameters variations of the process to be controlled.
The design of the PID controller is further done in the time domain. In the time domain, (33) can be written
as given in [2-23], because of the following:
The model parameters vary at every moment of time.
The adaptive controller must change its parameters also at every moment of time.
To convert (28) to the time domain, a differential operator is introduced [23]. In (36) represents the
cost function in (25), and 𝜃 = 𝑘𝑝, 𝑘𝑖, 𝑘𝑑.
Following [2-23], the representation of (33) in the time domain is:
)
(
))
(
1
(
)
(
)
(
)
(
)
(
)
( 2
t
g
k
p
t
g
k
t
g
k
p
t
r
t
g
k
t
g
pk
t
y
i
p
d
in
i
p
(34)
where: 𝑝 is a differential operator.
3.2.3. Determination of the error between the plant states and the reference model states
The tracking error of the closed-loop system and the reference model is [24, 25]:
𝜀(𝑡) = 𝑦(𝑡) − 𝑦𝑚(𝑡) (35)
Substituting (28) and (34) in (35) gives:
𝜀(𝑡) =
[𝑝𝑘𝑝𝑔(𝑡)+𝑘𝑖𝑔(𝑡)]𝑟𝑖𝑛(𝑡)
𝑝2𝑘𝑑𝑔(𝑡)+(1+𝑘𝑝𝑔(𝑡))𝑝+𝑘𝑖𝑔(𝑡)
−
𝜔𝑛
2
𝑝2+2𝜁𝜔𝑛𝑝+𝜔𝑛
2 𝑟𝑖𝑛(𝑡) (36)
12. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
311
ε(t) is the error between the transition behaviour of the closed-loop system and the reference model.
The criterion for optimization in (25) is given by (36).
3.2.4. Derivations of the expressions of the control parameters
The expressions of the sensitivity functions of the closed-loop system error towards the controller
parameters are derived by differentiating (36). These expressions are defined as
𝜕𝜀(𝑡)
𝜕𝑘𝑝
,
𝜕𝜀(𝑡)
𝜕𝑘𝑖
, and
𝜕𝜀(𝑡)
𝜕𝑘𝑑
.
They are derived as follows:
For
𝜕𝜀(𝑡)
𝜕𝑘𝑝
:
)
(
))
(
1
(
)
(
)
(
)
(
)
(
2
t
g
k
p
t
g
k
p
t
g
k
p
t
e
t
g
k
t
i
p
d
p
(37)
For
𝜕𝜀(𝑡)
𝜕𝑘𝑖
:
)
(
))
(
1
(
)
(
)
(
)
(
)
(
2
t
g
k
p
t
g
k
p
t
g
k
t
e
t
g
k
t
i
p
d
i
(38)
For
𝜕𝜀(𝑡)
𝜕𝑘𝑑
:
)
(
))
(
1
(
)
(
)
(
)
(
)
(
2
2
t
g
k
p
t
g
k
p
t
g
k
p
t
g
t
y
k
t
i
p
d
d
(39)
To make sure that there is a perfect tracking error, let assume that, the time behaviour of the process
is equal to the time behaviour of the reference model, as follows:
2
2
2
2
2
2
)
(
))
(
1
(
)
(
)
(
n
n
n
i
p
d p
p
t
g
k
p
t
g
k
p
t
g
k
t
g
The gradient method described in (26) is applied to find the expressions of the control parameters:
)
(
2
)
( 2
2
2
2
.
t
e
p
p
t
k
n
n
n
p
p
(40)
)
(
2
)
( 2
2
2
2
.
t
e
p
p
t
k
n
n
n
i
i
(41)
)
(
2
)
( 2
2
2
2
.
t
y
p
p
t
k
n
n
n
d
d
(42)
where: 𝛾𝑝, 𝛾𝑖 and 𝛾𝑑 are the adaptation gains.
The expression of the adaptive PID controller is the following:
)
(
2
)
(
2
)
(
)
(
)
(
2
)
(
)
(
2
2
2
2
2
2
2
2
2
2
2
2
t
y
p
p
t
t
e
p
p
t
t
e
p
p
t
t
u
n
n
n
d
n
n
n
i
n
n
n
p
13. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
312
4. SIMULATION RESULTS
The Simulink diagram of the closed-loop system with the derived adaptive PID controller is shown
in Figure 2. The simulation is done in MATLAB/Simulink environment. The closed-loop system is made of
the following subsystems:
Plant model represented as a second order transfer function.
Reference model.
Adaptation control algorithms.
Figure 2. Simulink diagram of the adaptive control algorithm based on the MIT rule
The simulations are done to evaluate the performance of the adaptive scheme under the following
parameters:
The set-points are: 10cm and 13cm.
The values of the adaptation gains are: 𝛾𝑝
= 28.5, 𝛾𝑖
= 1.18 × 10−3
and 𝛾𝑑
= −15.
A random disturbance of magnitude 0.8 and frequency 0.1 Hertz occurs at the output of the plant.
𝛽1
(valve ratio of the outlet pipe tank 1) and 𝛽2
(valve ratio of the outlet pipe of tank 2) are unknown, but
their values are bounded to a range of values.
𝛽1
varies within the following interval: [1.22522 1.9895]. The values: 1.3784; 1.5315195; 1.6081 and
1.6847 are selected randomly.
𝛽2
varies within the following interval: [0.545603 0.818405]. The values: 0.5661; 0.6820043; 0.6615
and 0.7502 are randomly selected.
4.1. Case 1: Simulation of the closed-loop adaptive system where there are no variations of
the parameters
The simulation shows the following results:
The graphs of the level of liquid 𝑦 in Tank 2.
𝛽1 (valve ratio of the outlet pipe tank 1) and 𝛽2 (valve ratio of the outlet pipe of tank 2) are at their
original values: 𝛽1 = 1.5315195 and 𝛽2 = 0.6820043.
Simulation results when the set point is 10cm as shown in Figure 3.
Simulation results when the set point is 13cm as shown in Figure 4.
The results of the simulations show that the designed model reference adaptive PID-controller has
excellent tracking performance, good rising time and it does not have any overshoot and steady state error.
14. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
313
Figure 3. Liquid level when there are no parameters variations; and the set-point is 10cm
Figure 4. Liquid level when there are no parameters variations; and the set-point is 13cm
4.2. Case 2: simulation of the closed-loop system when variations of the process parameters occur
4.2.1. Scenario 1
𝛽1
and 𝛽2
vary respectively from their original values to the following values: 1.3784 and 0.7502 at 60th
second of the simulation time and both values remain unchanged till the end of the simulation.
A conventional PI controller of parameters 𝐾𝑝 = 0.001 and 𝐾𝑖 = 4 is designed on MATLAB for the
model of the two-tank system.
The performance of the closed-loop system under normal PI controller is compared to the performance of
the closed-loop system under the MRAPIDC controller.
Simulation results when the set point is 10cm as shown in Figure 5
Simulation results when the set point is 13cm as shown in Figure 6
4.2.2. Scenario 2
Results when the closed-loop system is subjected to new parameters variations:
𝛽1
and 𝛽2
vary respectively from their original values to the following values: 1.6847 and 0.6615 at 90
seconds of the simulation time and both values remain unchanged till the end of the simulation.
Simulation results when the set point is 10cm as shown in Figure 7
Simulation results when the set point is 13cm as shown in Figure 8
15. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
314
Figure 5. Adaptive controller-based liquid level vs PI-controller based liquid level behaviour when
the parameters 𝛽1 and 𝛽2 respectively vary from their original values to: 1.3784 and 0.7502 at the 60th
second of the simulation time; and the set-point is 10cm
Figure 6. Adaptive controller-based liquid level vs PI-controller based liquid level behaviour when
the parameters 𝛽1 and 𝛽2 vary respectively from their original values to: 1.3784 and 0.7502 at the 60th
second of the simulation time; and the set-point is 13cm
Figure 7. Adaptive controller-based liquid level vs PI-controller based liquid level behaviour when the
parameters 𝛽1 and 𝛽2 vary respectively to 1.6847 and 0.6615 at the 90th
second of the simulation time;
and the set-point is 10cm
16. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
315
Figure 8. Adaptive controller-based liquid level vs PI-controller based liquid level behaviour when the
parameters 𝛽1 and 𝛽2 vary respectively to 1.6847 and 0.6615 at the 90th
second of the simulation time;
and the set-point is 13cm
In Table 2, a summary of the performance comparison between the closed-loop system with model-
reference Adaptive PID-controller and the closed-loop system with PI controller when the system is affected
by variations of the parameters is given. The results of the simulation show that the closed-loop system under
adaptive PID controller has better performance than the PI controller. The adaptive PID controller shows
stability and robustness with faster rise time and settling time. It also has a quicker recovery time when
variation of parameters occurs compared to the PI controller.
Table 2. Summary of the performance comparison between the closed-loop system with model reference
adaptive controller and the closed-loop system with PI controller when the two scenarios
of case 2 are considered
Set
point
Characteristics of the closed-loop system Case 2: Scenario 1
𝛽1 = 1.3784
𝛽2 = 0.7502
Case 2: Scenario 2
𝛽1 = 1.6847
𝛽2 = 0.6615
Adaptive PID
controller
PI Controller Adaptive PID
Controller
PI Controller
10cm Time delay 0 0 0 0
Overshoot when parameters vary -9.8% -10.02% 10% +10.01%
Rising time 1.95seconds 12.02seconds 1.95seconds 5.75seconds
Steady state error before parameters
variations
0 0 0 0
Steady state error when parameters
variations occur
-0.98 -1 1 +1.01
Settling time before parameters variations 22.4seconds 40.51seconds 25.03seconds 37.2seconds
Closed-loop system recovery time after
parameters variations
4.03seconds 30.11seconds 4.86seconds 29.31 seconds
13cm Time delay 0 0 0 0
Overshoot when parameters vary -10% -10.03% 10% 10.02%
Rising time 1.99seconds 9.4seconds 1.99seconds 10.71seconds
Steady state error before parameters
variations
0 0 0 0
Steady state error when parameter variations
occur
-1.3 -1.3 +1.3 +1.3
Settling time before parameters variations 15.79seconds 39.91seconds 18.64 seconds 39.83seconds
Closed-loop system recovery time after
parameters variations
3.05seconds 24.87seconds 3.41 seconds 29.35seconds
4.3. Performance comparison of the Pi-controller and the model reference adaptive PID-controller in
terms of random input disturbance rejection
The random disturbance has the following characteristics:
Amplitude: 0.8cm
17. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
316
Frequency: 0.1 Hertz
The Simulink block diagram of the closed-loop system with input disturbance is shown in Figure 9.
Figure 9. Simulink diagram for the comparison of the performance of the adaptive PID control algorithm and
the PI controller-based algorithm when a random disturbance occurs at the input of the plant
4.3.1. Simulation of the closed-loop system when parameters variations and a random input
disturbance occur at the 50th
seconds of the simulation time
𝛽1 and 𝛽2 vary respectively to the following values: 1.6081 and 0.5661 at the 50th second of
the simulation time and both values remain unchanged till the end of the simulation.
The characteristics of the output disturbance at the input of the plant are:
Amplitude: 1.4cm
Frequency: 0.25 Hertz
Simulation results when the set-point is 10cm as shown in Figure 10:
Simulation results when the set-point is 13cm as shown in Figure 11:
Figure 10. Adaptive controller-based liquid level vs PI-controller based liquid level when at the 50th
second
of the simulation time; 𝛽1 and 𝛽2 vary respectively from their original values to: 1.6081 and 0.5661,
at the same time a random input disturbance occurs and both remain unchanged till the end of the simulation;
the set-point is 10cm
The random disturbance has the following characteristics:
Amplitude: 200
Frequency: 0.1 Hertz
The Simulink block diagram of the closed-loop system with input disturbance is
shown in Figure (5.58).
Figure 5.58: Simulink diagram of the adaptive PID control algorithm based on MIT vs PI
controller-based algorithm when a random disturbance occurs at the input of the plant
Random disturbance
18. Int J Elec & Comp Eng ISSN: 2088-8708
Design of a model reference adaptive PID control… (Yohan Darcy Mfoumboulou)
317
Figure 11. Adaptive controller-based liquid level vs PI-controller based liquid level when at the 50th
second
of the simulation time; 𝛽1 and 𝛽2 vary respectively from their original values to: 1.6081 and 0.5661,
at the same time a random input disturbance occurs and both remain unchanged till the end of the simulation;
the set-point is 13cm
The results of the simulation show that the adaptive PID controller is robust to parameters’
variations and rejects input disturbance, while the conventional PI controller shows an unstable convergence
behaviour. The performance of the adaptive PID controller is outstanding.
4.4. Analysis of the design specification achievement
In the previous sections, the two-tank liquid level system is subjected to parameters’ variation and
random input disturbances, both are applied to the closed-loop system in different conditions.
The various simulations of the two-tank liquid level process reveal the following observations:
The adaptive PID controller and the system adapt to the changes of plant parameters.
The adaptive PID controller and the system reject random input disturbances.
The stability of the system is achieved.
The error signals go to zero.
The plant output always follows the reference model and the set-points trajectories regardless of the
bounded values of the varying parameters, and the introduction of random input disturbances.
5. CONCLUSION
In this paper, the first contribution is the design and derivation of the Adaptive PID controller based
on the MIT approach. Then, the performances of a standard PI controller designed on MATLAB and a new
model reference adaptive PID controller designed based on MIT technique are compared for a two-tank
process subjected to parameters variations and disturbances. The model reference adaptive PID controller can
stabilize the closed-loop system when variations of parameters occur at a speed eight times faster than the
conventional PI controller, which is a significant improvement.
The designed model reference adaptive PID controller showed outstanding performances and is
recommended as control method to be used for the stabilization of the closed-loop system subjected to
internal parameters variations and external random disturbances. For future research, the algorithm can be
improved to implement multiple model reference methodologies in real-time to push the boundaries of
the control algorithm.
REFERENCES
[1] J. Slotine, “Applied Nonlinear Control,” Prentice Hall, New Jersey, 1991.
[2] S. Coman, “Model Reference Adaptive Control for a Dc Electrical Drive,” Bulletin of the Transilvania University
of Brasov. Engineering Sciences. Series, vol. 6, no. 2, 2013.
[3] M. A Fellani and A. M. Gabaj, “PID Controller Design for Two Tanks Liquid Level Control System Using
Matlab,” International Journal of Electrical and Computer Engineering (IJECE), vol. 3, no. 3, pp. 436-442, 2015.
19. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 300 - 318
318
[4] P. Swarnkar, et al., “Effect of Adaptation Gain in Model Reference Adaptive Controlled Second Order System,”
Engineering, Technology & Applied Science Research, vol. 1, no. 3, pp. 70-75 2011.
[5] J. Vojtesek, et al., “Simulation of 1DOF and 2DOF adaptive control of the water tank model,” Proceedings - 29th
European Conference on Modelling and Simulation (ECMS), 2015.
[6] M. Navabi and H. Mirzaei, “Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter,” Latin
American Journal Solids and Structures, vol. 14, pp. 1040-1063, 2017.
[7] G. Nandhinipriyanka, et al., “Design of model reference adaptive controller for cylinder tank system,” International
Journal of Pure and Applied Mathematics, vol. 118, no. 20, 2018.
[8] M. S. Minhat, et al., “Adaptive control method for core power control in TRIGA Mark II reactor,” IOP Conference
Series: Materials Science and Engineering, 2018.
[9] M. H. A. Jalil, et al., “Model Reference Adaptive Controller without Integral (MRACWI) for Position Control of a
DC Motor,” Applications of modelling and simulation, vol. 3, pp. 18-27, 2019.
[10] A. N. K. Nasir, et al., “Performance comparison between sliding mode control (SMC) and PD-PID controllers
for a nonlinear inverted pendulum system,” World Academy of Science, Engineering and Technology, vol. 71,
pp. 400-405, 2010.
[11] A. Soltan, et al., “Fractional order PID system for supressing epileptic activities,” IEEE International Conference
on Applied System Invention (ICASI), pp. 338-341, 2018.
[12] M. A. Ahmad and R. R. Ismail, “A data-driven sigmoid-based PI controller for buck-converter powered DC
motor,” IEEE Synposium on Computer Applications and Industrial Electronics (ISCAIE), pp. 81-86, 2017.
[13] H. Senberber and A. Bagis, “Fractional PID controller design for fractional order systems using ABC algorithm,”
IEEE Electronics, pp. 1-7, 2017.
[14] M. R. B. Ghazali, et al., “Adaptive safe experimentation dynamics for data-driven neuroendocrine-PID control of
MIMO systems,” IETE Journal of Research, pp. 1-14, 2019.
[15] Y. D. Mfoumboulou and R. Tzoneva, “Development of a Model Reference Digital Adaptive Control Algorithm for
a Linearized Model of a Nonlinear Process,” International Journal of Applied Engineering Research, vol. 13,
no. 23, pp. 16662-16675, 2018.
[16] N. Winston, et al., “Design and performance comparison of different adaptive control schemes for pitch angle
control in a twin–rotor MIMO system,” International Journal of Electrical and Computer Engineering (IJECE),
vol. 9, no. 5, pp. 4114-4129, 2019.
[17] K. J. Astrom and B. Wittenmark, “Adaptive Control,” Second Edition, Dover Publications, 2008.
[18] N. Nise, “Control Systems Engineering Eighth Edition,” John Wiley & Sons, Limited, 2019.
[19] K. Ogata, “Modern Control Engineering Fourth Edition,” Upper Saddle River, New Jersey, Prentice Hall, 2002.
[20] Y. D Mfoumboulou, “Development of nonlinear control algorithms for implementation in distributed system,”
Master Thesis, Cape Peninsula University of Technology, 2014.
[21] MathWorks, “Control System toolbox user’s guide 2016a,” The MathWorks, Inc., 2016.
[22] A.B. Campo, “PID Control Design,” MATLAB - A Fundamental Tool for Scientific Computing and Engineering
Applications, vol. 1, pp. 3-18, 2012.
[23] S. Coman and C. Boldisor, “Adaptive Pi Controller Design to Control a Mass - Damper - Spring Process,” Bulletin
of the Transilvania University of Brasov, vol. 7, no. 2, 2014.
[24] T. Basten, et al., “Model-Based Design of Adaptive Embedded Systems,” Model-based design of adaptive
embedded systems. Verlag London: Springer, vol. 22, 2013.
[25] E. Kurniawan, et al., “Model-Based Control for Tracking and Rejection of Periodic Signals,” 7th International
Conference on Information Technology and Electrical Engineering (ICITEE), pp. 548-552, 2015.
BIOGRAPHY OF AUTHOR
Dr. Yohan Darcy Mfoumboulou is a post doctorate fellow at Cape Peninsula University of
Technology (CPUT). His research interests are in the fields of Classical and Modern Control,
Advanced Nonlinear Control, Real-Time Distributed Control Systems, Linear and Nonlinear
Adaptive Networked Control, Digital Adaptive Control and Process Control and
Instrumentation.