The dissolved oxygen concentration in the wastewater treatment process
(WWTP) must remain in a specific range while the factory operates. The
augmented positive identification (PID) controller with a nonlinear element
(sigmoid function) is proposed to assure stability and reduce uncertainties in
the wastewater direct reuse/recycling model. The nonlinear controller gains
(PID controller with sigmoid function) for uncertain wastewater treatment
processes are tuned using the particle swarm optimization (PSO) technique.
The proposed robust method for controlling wastewater treatment processes
has good robustness during model mismatching, reduces treatment time
compared to traditional positive identification (PID) controllers tuned by
PSO, is easy to apply, and has good performance, according to simulation
results.
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
HYBRID FUZZY LOGIC AND PID CONTROLLER FOR PH NEUTRALIZATION PILOT PLANTijfls
Use of Control theory within process control industries has changed rapidly due to the increase complexity
of instrumentation, real time requirements, minimization of operating costs and highly nonlinear
characteristics of chemical process. Previously developed process control technologies which are mostly
based on a single controller are not efficient in terms of signal transmission delays, processing power for
computational needs and signal to noise ratio. Hybrid controller with efficient system modelling is essential
to cope with the current challenges of process control in terms of control performance. This paper presents
an optimized mathematical modelling and advance hybrid controller (Fuzzy Logic and PID) design along
with practical implementation and validation of pH neutralization pilot plant. This procedure is
particularly important for control design and automation of Physico-chemical systems for process control
industry.
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.
Design of a model reference adaptive PID control algorithm for a tank system IJECEIAES
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.
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.
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.
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
HYBRID FUZZY LOGIC AND PID CONTROLLER FOR PH NEUTRALIZATION PILOT PLANTijfls
Use of Control theory within process control industries has changed rapidly due to the increase complexity
of instrumentation, real time requirements, minimization of operating costs and highly nonlinear
characteristics of chemical process. Previously developed process control technologies which are mostly
based on a single controller are not efficient in terms of signal transmission delays, processing power for
computational needs and signal to noise ratio. Hybrid controller with efficient system modelling is essential
to cope with the current challenges of process control in terms of control performance. This paper presents
an optimized mathematical modelling and advance hybrid controller (Fuzzy Logic and PID) design along
with practical implementation and validation of pH neutralization pilot plant. This procedure is
particularly important for control design and automation of Physico-chemical systems for process control
industry.
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.
Design of a model reference adaptive PID control algorithm for a tank system IJECEIAES
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.
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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 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.
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.
Tuning of PID controllers for integrating systems using direct synthesis methodISA Interchange
A PID controller is designed for various forms of integrating systems with time delay using direct synthesis method. The method is based on comparing the characteristic equation of the integrating system and PID controller with a filter with the desired characteristic equation. The desired characteristic equation comprises of multiple poles which are placed at the same desired location. The tuning parameter is adjusted so as to achieve the desired robustness. Tuning rules in terms of process parameters are given for various forms of integrating systems. The tuning parameter can be selected for the desired robustness by specifying Ms value. The proposed controller design method is applied to various transfer function models and to the nonlinear model equations of jacketed CSTR to show its effectiveness and applicability.
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...Scientific Review SR
This research paper aims at investigating disturbance rejection associated with a highly oscillating
second-order process. The PD-PI controller having three parameters are tuned to provide efficient rejection of a
step input disturbance input. Controller tuning based on using MATLAB control and optimization toolboxes.
Using the suggested tuning technique, it is possible to reduce the maximum time response of the closed loop
control system to as low as 0.0095 and obtain time response to the disturbance input having zero settling time.
The effect of the proportional gain of the PD-PI controller on the control system dynamics is investigated for a
gain ≤ 100. The performance of the control system during disturbance rejection using the PD -PI controller is
compared with that using a second-order compensator. The PD-PI controller is superior in dealing with the
disturbance rejection associated with the highly oscillating second-order process
PID controller for microsatellite yaw-axis attitude control system using ITAE...TELKOMNIKA JOURNAL
The need for effective design of satellite attitude control (SAC) subsystem for a microsatellite is imperative in order to guarantee both the quality and reliability of the data acquisition. A proportional-integral-derivative (PID) controller was proposed in this study because of its numerous advantages. The performance of PID controller can be greatly improved by adopting an integral time absolute error (ITAE) robust controller design approach. Since the system to be controlled is of the 4th order, it was approximated by its 2nd order version and then used for the controller design. Both the reduced and higher-order pre-filter transfer functions were designed and tested, in order to improve the system performance. As revealed by the results, three out of the four designed systems satisfy the design specifications; and the PD-controlled system without pre-filter transfer function was recommended out of the three systems due to its structural simplicity, which eventually enhances its digital implementation.
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.
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).
An intelligent hybrid control for paper machine systemeSAT Journals
Abstract
The aim of this paper is to present an intelligent hybrid controller for a paper machine system with ash content and dry weight as
outputs and filler valve and thick stock valve as inputs. Simulation studies have been carried out to check the robust performance
(10% increase in each process gain, 10% increase in each time delay, and 10% decrease in each time constant) of the system. The
improvement in the performance of proposed hybrid controller is compared with the Adaptive-neuro- fuzzy controller and Dahlin
controller and evaluated in terms of ISE.
Index Terms: Dahlin, Neuro-fuzzy, Hybrid and Robust
This document summarizes research on tuning an I-PD controller to control a highly oscillating second-order industrial process. The process has an 85.45% maximum overshoot and 8 second settling time. An I-PD controller is tuned using MATLAB optimization to minimize the integral of the squared error. The tuned controller completely cancels the overshoot and decreases the settling time to 1.46 seconds without undershoot, demonstrating improved performance over standard tuning techniques for controlling highly oscillating processes.
TUNING OF AN I-PD CONTROLLER USED WITH A HIGHLY OSCILLATING SECOND-ORDER PROC...IAEME Publication
This document summarizes research on tuning an I-PD controller to control a highly oscillating second-order process. It first describes the oscillating process and I-PD controller structure. It then details how the I-PD controller parameters were optimized using MATLAB to minimize integral square error, completely eliminating the process' 85.45% overshoot and reducing settling time from 8 to 1.46 seconds. For comparison, controller parameters were also calculated using standard ITAE tuning, resulting in lower overshoot and undershoot but longer settling time. The research demonstrates the I-PD controller's ability to improve control of highly oscillating processes.
Model-based Approach of Controller Design for a FOPTD System and its Real Tim...IOSR Journals
The document summarizes a study on model-based controller design for a first-order plus time delay (FOPTD) system. The study identifies the process model of a level control system using process reaction curve methods. Various tuning rules for internal model control-proportional integral derivative (IMC-PID) controllers from literature are applied to the system, including rules from Rivera, Chien, Lee, Skogestad, and Panda. The performance of each controller is evaluated based on rise time, settling time, percentage overshoot, integral absolute error, and integral of time multiplied absolute error. The study finds that the Panda tuning rule has the smallest percentage overshoot and integral absolute error, while the Chien rule has
Optimized proportional integral derivative (pid) controller for the exhaust t...Ali Marzoughi
The document describes using particle swarm optimization (PSO) to optimize the parameters of a proportional-integral-derivative (PID) controller for controlling the exhaust temperature of a gas turbine system. A new performance criterion called the multipurpose performance criterion (MPPC) is proposed that allows control of overshoot, rise time, and settling time by adjusting a weighting factor. The PSO algorithm is used to optimize the PID parameters by minimizing the MPPC. Results show the PSO-PID controller optimized with MPPC performs better than a conventional PID controller at achieving optimal transient response for the gas turbine exhaust temperature control system.
Design of Controllers for Liquid Level ControlIJERA Editor
The liquid level control system is commonly used in many process control applications. The aim of the process
is to keep the liquid level in the tank at the desired value. The conventional proportional-integral-derivative
(PID) controller is simple, reliable and eliminates the error rate but it cannot handle complex problems. Fuzzy
logic controllers are rule based systems which simulates human behavior of the process. The fuzzy controller is
combined with the PID controller and then applied to the tank level control system. This paper proposes Inverse
fuzzy with fuzzy logic controller for controlling liquid level system for a plant. This paper also compares the
transient response as well as error indices of PID, Fuzzy logic controller, inverse fuzzy controllers. The
responses of the controllers are verified through simulation. From the simulation results, it is observed that
inverse fuzzy-PID controller gives the superior performance than the other controllers. The inverse fuzzy-PID
controller gives better performance than the PID and fuzzy controller in terms of overshoot and settling time.
Performance analysis is carried out with Liquid Flow Control System Design with Fuzzy logic controller.
Results are evaluated by comparing the response time of conventional PID, fuzzy logic and Inverse fuzzy
controller. Comparative analysis of the performance of different controllers is done in MATLAB and Simulink.
This document summarizes a research paper that proposes using a genetic algorithm to optimize the tuning parameter for a two-degree-of-freedom internal model controller (TDF-IMC) designed for load frequency control in a power system. A second-order reduced model is obtained using Routh approximation to simplify the higher-order power system plant model. The genetic algorithm is used to find the optimal value of the tuning parameter for the TDF-IMC controller design, which results in an improved system response during disturbances and parameter variations compared to existing methods that do not optimize this parameter.
An Adaptive Liquid Level Controller Using Multi Sensor Data FusionTELKOMNIKA JOURNAL
This paper describes a design of adaptive liquid level control system using the concept of Multi
Sensor Data Fusion (MSDF). Purpose of the work is to design a controller for accurately controlling the
level of liquid in a process tank with liquid temperature changes. The proposed objective is obtained by i)
implementing a MSDF framework using Pau’s framework for measuring liquid level and temperature, ii)
analyzing the behavior of actuator output for variation in liquid temperature, and iii) designing a suitable
adaptive controller which will produce desired control action for controlling liquid level accurately using
neural network algorithms. Outputs from sensors are fused to obtain the fluid level output and also relation
of level transmitter output for change in temperature. This information is used by controller to train the
neural network so as to tune the controller parameters (proportional gain, integral constant, and differential
constant), to drive the actuator. Results obtained show that the system is able to control liquid level within
range of 1.915% of set point even with variations in liquid temperature.
Data-based PID control of flexible joint robot using adaptive safe experiment...journalBEEI
This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
Modeling and Control of MIMO Headbox System Using Fuzzy LogicIJERA Editor
The Headbox plays an important role in pulp supply system with sheet forming in paper making process. The air cushion headbox is a nonlinear & strong coupling system. In the air cushion headbox system there were two important parameters which include total head and the stock level for improving pulp product quality. These two parameters make this system MIMO output system so for this a decoupling controls strategy was required for interaction between these two control loops. In this paper fuzzy tuned PID control scheme is proposed for controlling the nonlinear control problem in air cushion headbox after the system being decoupled. An attempt has been made for comparison between classical (PID) and fuzzy tuned PID controller. It concludes that the fuzzy tuned PID controller is found most suitable for MIMO system in terms of obtaining steady state properties. The effects of disturbances are studied through computer simulation using Matlab/Simulink toolbox.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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 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.
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.
Tuning of PID controllers for integrating systems using direct synthesis methodISA Interchange
A PID controller is designed for various forms of integrating systems with time delay using direct synthesis method. The method is based on comparing the characteristic equation of the integrating system and PID controller with a filter with the desired characteristic equation. The desired characteristic equation comprises of multiple poles which are placed at the same desired location. The tuning parameter is adjusted so as to achieve the desired robustness. Tuning rules in terms of process parameters are given for various forms of integrating systems. The tuning parameter can be selected for the desired robustness by specifying Ms value. The proposed controller design method is applied to various transfer function models and to the nonlinear model equations of jacketed CSTR to show its effectiveness and applicability.
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...Scientific Review SR
This research paper aims at investigating disturbance rejection associated with a highly oscillating
second-order process. The PD-PI controller having three parameters are tuned to provide efficient rejection of a
step input disturbance input. Controller tuning based on using MATLAB control and optimization toolboxes.
Using the suggested tuning technique, it is possible to reduce the maximum time response of the closed loop
control system to as low as 0.0095 and obtain time response to the disturbance input having zero settling time.
The effect of the proportional gain of the PD-PI controller on the control system dynamics is investigated for a
gain ≤ 100. The performance of the control system during disturbance rejection using the PD -PI controller is
compared with that using a second-order compensator. The PD-PI controller is superior in dealing with the
disturbance rejection associated with the highly oscillating second-order process
PID controller for microsatellite yaw-axis attitude control system using ITAE...TELKOMNIKA JOURNAL
The need for effective design of satellite attitude control (SAC) subsystem for a microsatellite is imperative in order to guarantee both the quality and reliability of the data acquisition. A proportional-integral-derivative (PID) controller was proposed in this study because of its numerous advantages. The performance of PID controller can be greatly improved by adopting an integral time absolute error (ITAE) robust controller design approach. Since the system to be controlled is of the 4th order, it was approximated by its 2nd order version and then used for the controller design. Both the reduced and higher-order pre-filter transfer functions were designed and tested, in order to improve the system performance. As revealed by the results, three out of the four designed systems satisfy the design specifications; and the PD-controlled system without pre-filter transfer function was recommended out of the three systems due to its structural simplicity, which eventually enhances its digital implementation.
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.
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).
An intelligent hybrid control for paper machine systemeSAT Journals
Abstract
The aim of this paper is to present an intelligent hybrid controller for a paper machine system with ash content and dry weight as
outputs and filler valve and thick stock valve as inputs. Simulation studies have been carried out to check the robust performance
(10% increase in each process gain, 10% increase in each time delay, and 10% decrease in each time constant) of the system. The
improvement in the performance of proposed hybrid controller is compared with the Adaptive-neuro- fuzzy controller and Dahlin
controller and evaluated in terms of ISE.
Index Terms: Dahlin, Neuro-fuzzy, Hybrid and Robust
This document summarizes research on tuning an I-PD controller to control a highly oscillating second-order industrial process. The process has an 85.45% maximum overshoot and 8 second settling time. An I-PD controller is tuned using MATLAB optimization to minimize the integral of the squared error. The tuned controller completely cancels the overshoot and decreases the settling time to 1.46 seconds without undershoot, demonstrating improved performance over standard tuning techniques for controlling highly oscillating processes.
TUNING OF AN I-PD CONTROLLER USED WITH A HIGHLY OSCILLATING SECOND-ORDER PROC...IAEME Publication
This document summarizes research on tuning an I-PD controller to control a highly oscillating second-order process. It first describes the oscillating process and I-PD controller structure. It then details how the I-PD controller parameters were optimized using MATLAB to minimize integral square error, completely eliminating the process' 85.45% overshoot and reducing settling time from 8 to 1.46 seconds. For comparison, controller parameters were also calculated using standard ITAE tuning, resulting in lower overshoot and undershoot but longer settling time. The research demonstrates the I-PD controller's ability to improve control of highly oscillating processes.
Model-based Approach of Controller Design for a FOPTD System and its Real Tim...IOSR Journals
The document summarizes a study on model-based controller design for a first-order plus time delay (FOPTD) system. The study identifies the process model of a level control system using process reaction curve methods. Various tuning rules for internal model control-proportional integral derivative (IMC-PID) controllers from literature are applied to the system, including rules from Rivera, Chien, Lee, Skogestad, and Panda. The performance of each controller is evaluated based on rise time, settling time, percentage overshoot, integral absolute error, and integral of time multiplied absolute error. The study finds that the Panda tuning rule has the smallest percentage overshoot and integral absolute error, while the Chien rule has
Optimized proportional integral derivative (pid) controller for the exhaust t...Ali Marzoughi
The document describes using particle swarm optimization (PSO) to optimize the parameters of a proportional-integral-derivative (PID) controller for controlling the exhaust temperature of a gas turbine system. A new performance criterion called the multipurpose performance criterion (MPPC) is proposed that allows control of overshoot, rise time, and settling time by adjusting a weighting factor. The PSO algorithm is used to optimize the PID parameters by minimizing the MPPC. Results show the PSO-PID controller optimized with MPPC performs better than a conventional PID controller at achieving optimal transient response for the gas turbine exhaust temperature control system.
Design of Controllers for Liquid Level ControlIJERA Editor
The liquid level control system is commonly used in many process control applications. The aim of the process
is to keep the liquid level in the tank at the desired value. The conventional proportional-integral-derivative
(PID) controller is simple, reliable and eliminates the error rate but it cannot handle complex problems. Fuzzy
logic controllers are rule based systems which simulates human behavior of the process. The fuzzy controller is
combined with the PID controller and then applied to the tank level control system. This paper proposes Inverse
fuzzy with fuzzy logic controller for controlling liquid level system for a plant. This paper also compares the
transient response as well as error indices of PID, Fuzzy logic controller, inverse fuzzy controllers. The
responses of the controllers are verified through simulation. From the simulation results, it is observed that
inverse fuzzy-PID controller gives the superior performance than the other controllers. The inverse fuzzy-PID
controller gives better performance than the PID and fuzzy controller in terms of overshoot and settling time.
Performance analysis is carried out with Liquid Flow Control System Design with Fuzzy logic controller.
Results are evaluated by comparing the response time of conventional PID, fuzzy logic and Inverse fuzzy
controller. Comparative analysis of the performance of different controllers is done in MATLAB and Simulink.
This document summarizes a research paper that proposes using a genetic algorithm to optimize the tuning parameter for a two-degree-of-freedom internal model controller (TDF-IMC) designed for load frequency control in a power system. A second-order reduced model is obtained using Routh approximation to simplify the higher-order power system plant model. The genetic algorithm is used to find the optimal value of the tuning parameter for the TDF-IMC controller design, which results in an improved system response during disturbances and parameter variations compared to existing methods that do not optimize this parameter.
An Adaptive Liquid Level Controller Using Multi Sensor Data FusionTELKOMNIKA JOURNAL
This paper describes a design of adaptive liquid level control system using the concept of Multi
Sensor Data Fusion (MSDF). Purpose of the work is to design a controller for accurately controlling the
level of liquid in a process tank with liquid temperature changes. The proposed objective is obtained by i)
implementing a MSDF framework using Pau’s framework for measuring liquid level and temperature, ii)
analyzing the behavior of actuator output for variation in liquid temperature, and iii) designing a suitable
adaptive controller which will produce desired control action for controlling liquid level accurately using
neural network algorithms. Outputs from sensors are fused to obtain the fluid level output and also relation
of level transmitter output for change in temperature. This information is used by controller to train the
neural network so as to tune the controller parameters (proportional gain, integral constant, and differential
constant), to drive the actuator. Results obtained show that the system is able to control liquid level within
range of 1.915% of set point even with variations in liquid temperature.
Data-based PID control of flexible joint robot using adaptive safe experiment...journalBEEI
This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
Modeling and Control of MIMO Headbox System Using Fuzzy LogicIJERA Editor
The Headbox plays an important role in pulp supply system with sheet forming in paper making process. The air cushion headbox is a nonlinear & strong coupling system. In the air cushion headbox system there were two important parameters which include total head and the stock level for improving pulp product quality. These two parameters make this system MIMO output system so for this a decoupling controls strategy was required for interaction between these two control loops. In this paper fuzzy tuned PID control scheme is proposed for controlling the nonlinear control problem in air cushion headbox after the system being decoupled. An attempt has been made for comparison between classical (PID) and fuzzy tuned PID controller. It concludes that the fuzzy tuned PID controller is found most suitable for MIMO system in terms of obtaining steady state properties. The effects of disturbances are studied through computer simulation using Matlab/Simulink toolbox.
Similar to Robustness enhancement study of augmented positive identification controller by a sigmoid function (20)
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
K-centroid convergence clustering identification in one-label per type for di...IAESIJAI
Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
Plant leaf detection through machine learning based image classification appr...IAESIJAI
Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
Backbone search for object detection for applications in intrusion warning sy...IAESIJAI
In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Deep neural network for lateral control of self-driving cars in urban environ...IAESIJAI
The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray i...IAESIJAI
Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions. Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
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Robustness enhancement study of augmented positive identification controller by a sigmoid function
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 2, June 2023, pp. 686~695
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i2.pp686-695 686
Journal homepage: http://ijai.iaescore.com
Robustness enhancement study of augmented positive
identification controller by a sigmoid function
Abbas H. Issa1
, Sarab A. Mahmood1
, Abdulrahim T. Humod1
, Nihad M. Ameen2
1
Department of Electrical Engineering, University of Technology, Baghdad, Iraq
2
Department of Communication Engineering, University of Technology, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received Mar 19, 2022
Revised Oct 14, 2022
Accepted Nov 13, 2022
The dissolved oxygen concentration in the wastewater treatment process
(WWTP) must remain in a specific range while the factory operates. The
augmented positive identification (PID) controller with a nonlinear element
(sigmoid function) is proposed to assure stability and reduce uncertainties in
the wastewater direct reuse/recycling model. The nonlinear controller gains
(PID controller with sigmoid function) for uncertain wastewater treatment
processes are tuned using the particle swarm optimization (PSO) technique.
The proposed robust method for controlling wastewater treatment processes
has good robustness during model mismatching, reduces treatment time
compared to traditional positive identification (PID) controllers tuned by
PSO, is easy to apply, and has good performance, according to simulation
results.
Keywords:
Augmented proportional-
integral-derivative controller
Particle swarm optimization
Robustness
Sigmoid-function
Wastewater treatment process
This is an open access article under the CC BY-SA license.
Corresponding Author:
Abbas H. Issa
Department of Electrical Engineering, University of Technology
Baghdad, Iraq
Email: abbas.h.issa@uotechnology.edu.iq
1. INTRODUCTION
In wastewater treatment plant (WWTP), dissolved oxygen concentration has a direct impact on the
performance of the WWTP [1]. In order to obtain wastewater with a substrate concentration within the legal
standard limit values (below 20 mg/l), quantitative feedback theory (QFT) technology control is used [2], [3].
Variable operating regimes were allowed within large limits. General rain, normal rain, and drought were the
three basic regimes evaluated. A QFT controller that assures excellent properties for the three regimes is
indicated [4]. Significant development has been made in the field of control technology in recent decades,
particularly in the control of dissolved oxygen and the procedures of comparative evaluation of wastewater
treatment plant control systems [5], [6].
In the rule structure of an internal model, in an activated sludge process (ASP) based wastewater
treatment, virtual reference adjustment feedback is used to regulate dissolved oxygen emissions and substrate
concentration [7], [8]. The methodology of data-driven proved to be easier to implement and provided a
better result compared to continuous-time proportional integral (PI) controllers with two degrees of freedom
[9]. A resilient positive identification (PID) controller can guarantee stability and be robust and economical
in model mismatch circumstances [10]. The fractional order proportional-integral (FOPI)controller design
scheme for the aeration model of the 2nd order activated sludge wastewater treatment process plus process
aeration control activated sludge wastewater treatment time delay performance [11], [12]. The radial basis
function neural network-based PID (RBFNNPID) algorithm in gradient descent technique suggests and
simulates an adaptive PID algorithm based on the radial basis function (RBF) based on the neural network
(NN) for the optimal control of dissolved oxygen in a sludge activation process, the comparison of the
2. Int J Artif Intell ISSN: 2252-8938
Robustness enhancement study of augmented positive identification controller by … (Abbas H. Issa)
687
performance simulation results for the conventional PID with the RBFNNPID control algorithm to keep
dissolved oxygen concentrations show that the RBFNNPID better performance results can be achieved. The
RBFNNPID control algorithm has good tracking, anti-interference, and excellent robustness performance
[13]. A fuzzy predictive control law is used as a control strategy for the treatment process of wastewater [14].
A PID controller is used in a hybrid controller, as well as a fuzzy logic controller (FLC) and a fuzzy-PID
supervised, in which the PID's parameters are updated using a fuzzy system [15].
A metaheuristic search technique that employs process simulation blocks in a black-box approach is
used to build a heuristic control strategy for non-linear multivariable systems, with the location and range of
the search region changing adaptively during the algorithm's iterations [16], [17]. The recommended self-
organizing radial basis function (SORBF) regulating dissolved oxygen concentration in a WWTP may
change its structure dynamically to maintain forecast accuracy. It is based on the self-organizing radial basis
function model predictive control (SORBF-MPC) approach, which uses a self-organizing RBF neural
network model for predictive control [18]. For WWTP, the simplification model is created by simplifying the
activated sludge model; it is an approach to synthesizing H∞ resilient PIDs, and the ideal PID controller
parameters bound by H∞ requirements are adapted using an evolutionary algorithm to the various
disturbances; the simulation shows that the closed-loop WWTP meets a variety of H∞ criteria, has good
tracking capabilities, and can withstand noise disturbances [19]. In a fractional order PID controller using the
multi-objective optimization function, the weighted integral time absolute error of individual loops is added
together, and the performance rejection is validated by analyzing the response for set point change and
interruption [20]. Through MATLAB simulations, a well-tuned baseline multi-loop PID controller was
compared to the fuzzy inference baseline sliding methodology and showed that it could simultaneously
regulate fuel ratios to appropriate levels under varying airflow disturbances by adjusting the mass flow rates
of the port fuel injection (PFI) and direct-injection (DI) engines [21].
The complexity and non-linearity of WWTP represent a significant challenge in developing viable
processes for control technologies. Wastewater treatment processes are non-linear and, due to influencing
factors, show many uncertainties that make selecting the structure and parameter model difficult. The set
point of the dissolved oxygen in the control system is adjusted according to the influent system [1]–[3].
This work proposes a wastewater treatment system with an augmented PID controller. The dissolved
oxygen level for the organic substrate is controlled by using a non-linear element (sigmoid function). The
algorithm of particle swarm optimization (PSO) is utilized to obtain the gains of the PID controller and the
augmented part; the robustness of the PID controller is increased by using the augmented element.
2. WASTEWATER TREATMENT PROCESS
For the process of wastewater treatment, three main regimes related to weather conditions are
considered: rain, normal, and drought to control the level of dissolved oxygen in the tank to ensure the
allowable level of organic substrate. Three main regimes are considered with restrictions due to extreme
situations and the variation of the parameter model due to process variables (e.g., temperature). The
following is a second-order transfer function that can be used to depict the process as illustrated in (1) [22],
𝐺𝑛(𝑠) = 𝑘𝑛
(𝑠+𝑎𝑛)
(𝑠+𝑏𝑛)(𝑠+𝑐𝑛)
(1)
where, 𝑛 is integer number representing the regime type. Table 1 represents the transfer function parameters
for three regimes with upper and lower limit (min and max) values for each parameter.
Table 1. Wastewater processes parameters [23]
Regimes Parameter 𝑘𝑛 Parameter 𝑎𝑛 Parameter 𝑏𝑛 Parameter 𝑐𝑛
Rain (𝑛=1) K1[9.5 10.5] 𝑎1[2.0 3.5] 𝑏1[1.5 2.0] 𝑐1[0.3 0.4]
Normal (𝑛=2) K2[10.511.5] 𝑎2[3.0 4.5] 𝑏2[1.0 1.5] 𝑐2[0.4 0.5]
Drought (𝑛=3) K3[9.0 10.5] 𝑎3[4.5 5.5] 𝑏3[0.5 1.0] 𝑐3[0.3 0.4]
3. PID CONTROLLER
The proposed conventional PID controller's transfer function to increase the dynamic system's
response is given by (2) [24],
𝑄(𝑠)
𝐸(𝑠)
= K1 +
K2
𝑠
+ K3
𝐾4 s
𝑠+𝐾4
(2)
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688
where K1, K2, K3, and K4 are proportional, integral, derivative, and filter gains are the four types of gains
respectively. A sigmoid function is added to the typical PID controller yields the nonlinear PID controller.
The control signal is illustrated in (3),
𝑢(𝑡) = (
2
1+𝑒−𝐾5𝑄(𝑡) − 1) (3)
where K5 is the sigmoid function's gain and 𝑄(𝑡) is the standard PID's output. Figure 1 shows the nonlinear
PID controller construction. The design of PID controller need to tune the controller parameters (gains) to
satisfy the desired specifications for the output response, therefore, PSO algorithm is used as an optimal
tuning method for PID gains.
Figure 1. Augmented PID controller structure
4. PARTICLE SWARM OOPTIMIZATION TECHNIQUE
The recognized PSO algorithm's equations are shown as [25], [26],
𝑉
𝑖,𝑗
(𝑝+1)
= 𝑊 ∗ 𝑉
𝑖,𝑗
(𝑝)
+ 𝑐1 ∗ 𝑅1 ∗ (Pbest𝑖 − 𝑋𝑖,𝑗
(𝑝)
) + 𝑐2 ∗ 𝑅2 ∗ (Gbest𝑖 − 𝑋𝑖,𝑗
(𝑝)
) (4)
𝑋𝑖,𝑗
(𝑝+1)
= 𝑋𝑖,𝑗
(𝑝)
+ 𝑉
𝑖,𝑗
(𝑝+1)
(5)
𝑖 = 1, 2, … , 𝑚 𝑗 = 1, 2, … , 𝐿
where 𝑚, 𝐿, and 𝑝 are the number of particles, variables (parameters), and iteration respectively, Vi,j
(p)
, XI,j
(p)
are the velocity, position of 𝑖𝑡ℎ
particles at iteration 𝑝, 𝑋𝑝+1
is updated position, 𝑉𝑝+1
is updated velocity,
Pbsti
is the position of best 𝑖𝑡ℎ
particles, Gbst is the best particles of the population, 𝑊 is the weight factor,
𝑐1, 𝑐2 are constants, 𝑅1, 𝑅2 are a random numbers.
The used parameters in this work are: 𝑛 = 12, 𝐿 = 4 for conventional PID controller, 𝐿 = 5 for
nonlinear PID controller, 𝑐1 = 1.1, 𝑐2 = 1.2, 𝑊 = 0.9 − (
0.4
80
) ∗ 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑛𝑢𝑚𝑏𝑒𝑟, and maximum
iteration number equal to 80. The utilized fitness of integral time square error interactive technology and
smart education (ITSE) is,
FIT = ∫ 𝑡 𝑒2(𝑡)
𝑇
0
d𝑡 (6)
where, 𝑒 is the error signal between the output response and the desired response.
5. RESULTS AND DISCUSSION
Simulation for a wastewater treatment process can be classified into two phases: the tuning of
controller parameters and the control phase. The PSO algorithm is used as an optimal tuning method for PID
gains. The MATLAB/SIMULINK program for wastewater treatment process (normal regime for lower
limit), conventional PID controller, and fitness function is illustrated in Figure 2. The changes in
conventional PID gains and fitness value according to iteration number for a normal regime with a lower
limit using the PSO algorithm are depicted in Figures 3 and 4, respectively. Table 2 shows the typical PID
controller gains tuned by the PSO algorithm for different regimes.
4. Int J Artif Intell ISSN: 2252-8938
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Figure 2. Wastewater process with conventional PID controller and fitness function
Figure 3. Tuning gains for conventional PID controller using the PSO algorithm
Figure 4. Changing of fitness value using PSO algorithm
Table 2. Gains of PID and fitness values obtained from PSO algorithm
PID Controller Gains Regime at Rain Regime at Normal Regime at Drought
K1 0.216 0.1027 0.0504
K2 0.0639 0.0338 0.0105
K3 0.0295 0.0456 0.054
K4 0.6616 1.9175 3.0621
Fitness value 0.00077 0.00051 0.00055
The steps response of the conventional PID controller designed for the drought regime (lower limit)
is illustrated in Figure 5. Figures 5-7 show that the obtained responses for the drought regime (lower limit),
normal regime (lower limit), and rain regime (lower limit), respectively, are the same as the desired response,
5. ISSN: 2252-8938
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690
and the other responses deviate from the desired response according to the process environment. The
deviation for a drought regime concerning long settling time and the deviation for a normal regime and rain
are regarding settling time and large overshoot. The non-linear PID controller gains for rain regime (lower
limit) tuned by the PSO algorithm are illustrated in Figure 8. The non-linear gains and fitness values for
different regimes obtained from the PSO algorithm are depicted in Table 3.
Figure 5. Step response for WWTP using PID controller for drought regime
Figure 6. Step response for WWTP using PID controller for normal regime
Figure 7. Step response for WWTP using PID controller for rain regime
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Figure 8. Gains values of nonlinear PID controller utilizing the PSO technique for rain regime
Table 3. The gains and fitness values for nonlinear PID controller
PID Controller gains Plant at rain Plant at normal Plant at drought
K1 2.4523 1.2964 0.3872
K2 0.715 0.423 0.0769
K3 0.304 0.5723 0.4077
K4 0.7085 1.9233 3.0212
K5 0.1783 0.1595 0.2541
Fitness value 0.00075 0.0005 0.00039
The step response for three regimes of wastewater process with a non-linear PID controller designed
for a normal regime with sigmoid function gain (K5=0.1595) is illustrated in Figure 9, which looks like the
response of a conventional PID controller in Figure 6. To enhance the robustness of the controller, it is
possible to increase the sigmoid function gain (K5). The enhancement of the robust response is obviously
seen in Figure 10 for K5=2, and Figure 11 for K5=10. The step responses for the non-linear PID controller
designed for rain and drought regimes with gain (K5=10) are shown in Figure 12 and Figure 13 respectively,
which depict the high robustness of the proposed non-linear PID controller by comparison with the responses
of Figure 5 and Figure 7.
Figure 9. Step response for nonlinear PID controller of normal regime with gain (K5=0.1595)
Figure 14 represents the white noise signal that was injected into the system for the purpose of
checking the robustness of the system against unwanted signals. Figure 15 shows the output responses for the
control system in the regular regime using the conventional PID controller and the augmented PID controller
with (K5=10) under the influence of disturbance (white noise). The response of the WWTP with a
conventional controller is stable with perturbations of about ±15%, and the response of the WWTP with PID
7. ISSN: 2252-8938
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692
augmented by a sigmoid function of gain (K5=10) is stable. It has perturbations of about ±2%. That means
the augmented PID controller has a better disturbance reduction than the non-augmented PID controller.
Figure 15 shows that the augmented PID's robustness is better than the non-augmented PID controller.
Figure 10. Step response for nonlinear PID controller of normal regime with gain (K5=2)
Figure 11. Step response for nonlinear PID controller of normal regime with gain (K5=10)
Figure 12. Step response for nonlinear PID controller of rain regime with gain (K5=10)
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Figure 13. Step response for nonlinear PID controller of drought regime with gain (K5=10)
Figure 14. Injected white noise to WWTP
Figure 15. Step response of WWTP with augmented and non-augmented PID controllers
0 5 10 15 20 25 30 35 40 45 50
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
TIME (second)
noise
value
0 5 10 15 20 25 30 35 40 45 50
0
0.2
0.4
0.6
0.8
1
1.2
1.4
TIME (second)
Dissolved
oxygen
PID controller
Augmented PID controller
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6. CONCLUSION
The wastewater treatment process is uncertain and non-linear. It needs a robust controller to reduce
the influence of parameter uncertainty and non-linearity. A robust controller is also required to reduce
the influence of uncertainties and stabilize the response of the open-loop system. The conventional and
non-linear PID controller gains are found using the PSO algorithm. The wastewater treatment plant is
mentioned under three different regimes, and the comparison result shows the robustness of the designed
controller. The augmented PID controller has less influence from disturbance than the non-augmented PID
controller. The augmentation of a non-linear function to the PID controller allows the system to become more
robust than conventional PID controllers. Also, the proposed non-linear controller has the advantage of
increasing the robustness by increasing the gain of the sigmoid function only.
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BIOGRAPHIES OF AUTHORS
Abbas H. Issa holds a degree of Ph. D in Control and Automation Engineering
from University of Technology, Iraq in 2002. He also received his B.Sc. and M.Sc.
(Nuclear Engineering) from Baghdad University, Iraq in 1989 and 1995, respectively. He is
currently an assistance professor at Electrical Engineering Department in University of
Technology, Baghdad, Iraq since September 2007. His research includes adaptive control,
optimal control, Robotic, fault tolerant control, intillegent control and robust control. He
has published over 30 papers in international journals and conferences. He can be contacted
at email: abbas.h.issa@uotechnology.edu.iq.
Sarab A. Mahmood B.Sc. in Electrical and Electronic Engineering, Iraq in
2004. M.Sc. in Electrical Power Engineering from South Russian State Polytechnic
University, Russia in 2016. Director of the Quality Division, Electrical Engineering
Department, University of Technology-Iraq. She can be contacted at email:
sarab.a.mahmood@uotechnology.edu.iq.
Abdulrahim T. Humod: Mahmood B.Sc. in Electrical and Electronic
Engineering, Military engineering college, Iraq, respectively in 1984. He received his M.Sc.
in 1990 (control and guidance) from the Military engineering college, Iraq. Ph.D. from
Military engineering college (control and guidance) in 2002. He is now an Asst. Professor
in University of Technology, Iraq. His research interests are control and guidance. He can
be contacted at email: 30040@uotechnology.edu.iq.
Nihad M. Ameen: Mahmood Asst. Professor in University of Technology-
Iraq, Communication Engineering Department, University of Technology Baghdad, Iraq.
Received his B.Sc. in the Department of Mechatronic Engineering IN 1994, received his
M.Sc. in 1990, Ph.D. from Russia in (Mechatronics and Robot techniques) in 2017.
Research interests: mechatronics, communication and computer control. The number of
publications is 29. He can be contacted at email: nihad.m.ameen@uotechnology.edu.iq.