This paper gives the comparative study among various techniques used to control the speed of three phase induction motor. In this paper, indirect vector method is used to control the speed of Induction motor. Firstly Simulink Model is developed by using MATLAB/ Simulink software. PI controller, Fuzzy PI Hybrid controller, Genetic Algorithm (GA) are the techniques involved in control Induction motor and the results are compared. By converting three phase supply currents coming from stator to Flux and Torque components of current the speed responses such as rise time, overshoot, settling time and speed regulation at load have been observed and compared among the techniques. The PI controller parameters defined by an objective function are calculated by using Genetic Algorithms presented good performance compared to Fuzzy PI Hybrid controller which has parameters chosen by the human operator.
This document presents a sensor-less speed control method for induction motors using alpha-cut fuzzy logic field oriented control (FOC). It develops a non-linear induction motor model that accounts for errors in flux estimation. FOC is used to decouple the current components for flux generation and torque generation. Clarke and Park transformations are applied to control the d-q axis currents for flux and torque regulation using PI controllers. Simulations show the motor speed closely follows the reference speed even under load, maintaining stability. The stator current and electromagnetic torque responses indicate the control system effectively regulates load current and torque.
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.
Speed control of Separately Excited DC Motor using various Conventional Contr...IJERA Editor
This paper presents comparative study of various conventional controllers such as Proportional (P), Proportional
Derivative (PD), Proportional integral (PI) and Proportional Integral Derivative (PID) controller for a speed
control of a Separately Excited Direct Current (SEDC) motor by using MATLAB / SIMULINK. All controllers
have their specific function for a particular task. The speed control normally done by feedback loop or closed
loop. The aim of development of this paper is towards providing efficient and simple method for controlling the
speed. The auto - tuning method are used to for this paper to control the speed. Among all this controllers PI
controller are frequently utilized in industries as compared to PID because Derivative action are sensitive to
noise, though PID controller will improve the steady state error. Additionally it produces less overshoot,
decreasing rise time and settling time. The MATLAB simulation are analysed and compared by using the auto
tuning method.
Keywords - Proportional (P), Proportional – Derivative (PD), Proportional - Integral (PI), Proportional –
For Induction motor is a system that works at their speed, nevertheless there are applications at which the speed operations are needed. The control of range of speed of induction motor techniques is available. The robust control is used with induction motor and the performance of the system with the controller will be improved. The mathematical model to the controller, which were coded in MATLAB. The modeling and controller will be shown by the conditions of robustness of be less than one.
This document compares the performance of indirect vector control of an induction motor using proportional-integral (PI) and proportional-integral-derivative (PID) speed controllers. It first provides background on induction motors, vector control techniques, and PI/PID controllers. It then presents the simulation model and results, which show the PID controller provides better speed response characteristics like shorter settling time. In conclusion, the PID controller improves the speed performance for indirect vector control of an induction motor drive.
This document describes a study comparing different speed control methods for a separately excited DC motor using MATLAB simulation. It develops a mathematical model of the DC motor and designs proportional-integral-derivative (PID), internal model control (IMC), and fuzzy logic controllers. It then simulates the performance of each controller and analyzes the step response results. The fuzzy logic controller provided the fastest rise time and lowest overshoot compared to the PID and IMC controllers.
Speed Sensorless Vector Control of Induction Motor Drive with PI and Fuzzy Co...IJPEDS-IAES
This document describes a study that compares speed sensorless vector control of an induction motor drive using PI and fuzzy logic controllers. A natural observer estimates the speed and rotor fluxes of the induction motor without direct feedback. Load torque is estimated using an adaptive method. Both PI and fuzzy controllers are used to control motor speed in closed loop. Simulation results in MATLAB show that the fuzzy controller has better performance in terms of torque ripples compared to the PI controller under various running conditions. Experimental results are also provided to validate the natural observer based sensorless control method using a PI controller.
This document presents a seminar on field oriented control of induction motors. It discusses direct and indirect field oriented control, with direct being better at high speeds but indirect not requiring additional sensors. It also discusses applications in industries like pumps, fans, conveyors and elevators. The document presents Matlab simulations comparing control methods, showing fuzzy logic control has better speed and torque response than PI control, with improved performance at low speeds. It concludes the hybrid model using current estimation could improve indirect control at low speeds.
This document presents a sensor-less speed control method for induction motors using alpha-cut fuzzy logic field oriented control (FOC). It develops a non-linear induction motor model that accounts for errors in flux estimation. FOC is used to decouple the current components for flux generation and torque generation. Clarke and Park transformations are applied to control the d-q axis currents for flux and torque regulation using PI controllers. Simulations show the motor speed closely follows the reference speed even under load, maintaining stability. The stator current and electromagnetic torque responses indicate the control system effectively regulates load current and torque.
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.
Speed control of Separately Excited DC Motor using various Conventional Contr...IJERA Editor
This paper presents comparative study of various conventional controllers such as Proportional (P), Proportional
Derivative (PD), Proportional integral (PI) and Proportional Integral Derivative (PID) controller for a speed
control of a Separately Excited Direct Current (SEDC) motor by using MATLAB / SIMULINK. All controllers
have their specific function for a particular task. The speed control normally done by feedback loop or closed
loop. The aim of development of this paper is towards providing efficient and simple method for controlling the
speed. The auto - tuning method are used to for this paper to control the speed. Among all this controllers PI
controller are frequently utilized in industries as compared to PID because Derivative action are sensitive to
noise, though PID controller will improve the steady state error. Additionally it produces less overshoot,
decreasing rise time and settling time. The MATLAB simulation are analysed and compared by using the auto
tuning method.
Keywords - Proportional (P), Proportional – Derivative (PD), Proportional - Integral (PI), Proportional –
For Induction motor is a system that works at their speed, nevertheless there are applications at which the speed operations are needed. The control of range of speed of induction motor techniques is available. The robust control is used with induction motor and the performance of the system with the controller will be improved. The mathematical model to the controller, which were coded in MATLAB. The modeling and controller will be shown by the conditions of robustness of be less than one.
This document compares the performance of indirect vector control of an induction motor using proportional-integral (PI) and proportional-integral-derivative (PID) speed controllers. It first provides background on induction motors, vector control techniques, and PI/PID controllers. It then presents the simulation model and results, which show the PID controller provides better speed response characteristics like shorter settling time. In conclusion, the PID controller improves the speed performance for indirect vector control of an induction motor drive.
This document describes a study comparing different speed control methods for a separately excited DC motor using MATLAB simulation. It develops a mathematical model of the DC motor and designs proportional-integral-derivative (PID), internal model control (IMC), and fuzzy logic controllers. It then simulates the performance of each controller and analyzes the step response results. The fuzzy logic controller provided the fastest rise time and lowest overshoot compared to the PID and IMC controllers.
Speed Sensorless Vector Control of Induction Motor Drive with PI and Fuzzy Co...IJPEDS-IAES
This document describes a study that compares speed sensorless vector control of an induction motor drive using PI and fuzzy logic controllers. A natural observer estimates the speed and rotor fluxes of the induction motor without direct feedback. Load torque is estimated using an adaptive method. Both PI and fuzzy controllers are used to control motor speed in closed loop. Simulation results in MATLAB show that the fuzzy controller has better performance in terms of torque ripples compared to the PI controller under various running conditions. Experimental results are also provided to validate the natural observer based sensorless control method using a PI controller.
This document presents a seminar on field oriented control of induction motors. It discusses direct and indirect field oriented control, with direct being better at high speeds but indirect not requiring additional sensors. It also discusses applications in industries like pumps, fans, conveyors and elevators. The document presents Matlab simulations comparing control methods, showing fuzzy logic control has better speed and torque response than PI control, with improved performance at low speeds. It concludes the hybrid model using current estimation could improve indirect control at low speeds.
Speed Control of Induction Motor using FOC MethodIJERA Editor
This document presents a method for speed control of induction motors using field oriented control (FOC). FOC works by controlling the flux and torque producing currents in the motor separately. It describes the FOC algorithm which involves transforming currents between stationary and rotating reference frames to allow proportional-integral control of flux and torque. Simulation results show that a PI controller provides fast, accurate speed control of an induction motor model across its full operating range with minimal overshoot. FOC allows induction motors to operate smoothly at variable speeds like DC motors and provides benefits like reduced motor size and cost.
Speed Control of DC Motor using PID Controller for Industrial Applicationijtsrd
This paper is to design PID controller to supervise and control the speed response of the DC motor and MATLAB program is used for industrial application . PID controllers are widely used in a industrial plants because of their simplicity and robustness. The results obtained from simulation are approximately similar to that obtained by practical. Also the dynamic behavior is studied. Manoj Kumar Ranwa | Ameen Uddin Ahmad "Speed Control of DC Motor using PID Controller for Industrial Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26599.pdfPaper URL: https://www.ijtsrd.com/engineering/electrical-engineering/26599/speed-control-of-dc-motor-using-pid-controller-for-industrial-application/manoj-kumar-ranwa
Speed control of dc motor using relay feedback tuned piAlexander Decker
This document discusses speed control of a DC motor using different controller types, including a relay feedback tuned PI controller, fuzzy PI controller (FPIC), and self-tuned fuzzy PI controller (STFPIC). The FPIC and STFPIC are developed using fuzzy logic to overcome limitations of conventional PID controllers for nonlinear systems without an accurate mathematical model. An experimental setup is used to test the controllers' performance on a DC motor. Results show the model-independent STFPIC and FPIC controllers improve speed control performance compared to the relay-tuned PI controller.
TORQUE CONTROL OF AC MOTOR WITH FOPID CONTROLLER BASED ON FUZZY NEURAL ALGORITHMijics
Nowadays in the complicated systems, design of proper and implementable controller has a most importance. With respect to ability of fractional order systems in complicated systems identification as a first order fractional system with time delay, usage of fractional order PID has a proper result. From one side flexibility of fractional calculus than integer order has been topics of interest to the researchers. From another side, PMSM motors which are one the AC motor types, has been allocated largely accounted position in industry and used in variety applications. Therefore in this paper torque direct control of PMSM motors with FOPID based on model is proposed. Also fuzzy neural controllers are widely considered. Reason of this is success of fuzzy neural controller in control and identification of uncertain and complicated systems. The proposed method in this paper is combination of FOPID controller with fuzzy neural supervision system which with coefficients setting of this controller, control operation of PMSM will improve. Results of proposed method show the ability of proposed technique in reference signal tracking, elimination of disturbances effects and functional robustness in presence of noise and uncertainty. The results show the error averagely in three condition, nominal form, step disturbance and noise and uncertainly will decrease 11.66% in proposed method (FNFOPID) with Integral Square Error criterion and 7.69% with Integral Absolute Error criterion in comparison to FOPID.
A New Induction Motor Adaptive Robust Vector Control based on Backstepping IJECEIAES
In this paper, a novel approach to nonlinear control of induction machine, recursive on-line estimation of rotor time constant and load torque are developed. The proposed strategy combines Integrated Backstepping and Indirect Field Oriented Controls. The proposed approach is used to design controllers for the rotor flux and speed, estimate the values of rotor time constant and load torque and track their changes on-line. An open loop estimator is used to estimate the rotor flux. Simulation results are presented which demonstrate the effectiveness of the control technique and on-line estimation.
This paper introduces experimental comparison study between six and four switch inverter fed three phase induction motor drive system. The control strategy of the drive is based on speed sensoreless vector control using model reference adaptive system as a speed estimator. The adaptive mechanism of speed control loop depends on fuzzy logic control. Four switch inverter conFigureurations reduces the cost of the inverter, the switching losses, the complexity of the control algorithms, interface circuits, the computation of real-time implementation, volume-compactness and reliability of the drive system. The robustness of the proposed model reference adaptive system based on four switch three-phase inverter (FSTPI) fed induction motor drive is verified experimentally at different operating conditions. Experimental work is carried using digital signal processor (DSP1103) for a 1.1 kW motor. A performance comparison of the proposed FSTP inverter fed IM drive with a conventional six switch three-phase inverter (SSTP) inverter system is also made in terms of speed response. The results show that the proposed drive system provides a fast speed response and good disturbance rejection capability. The proposed FSTP inverter fed IM drive is found quite acceptable considering its performance, cost reduction and other advantages features.
Simulation DC Motor Speed Control System by using PID Controllerijtsrd
Speed control system is the most common control algorithm used in industry and has been universally accepted in industrial control. One of the applications used here is to control the speed of the DC motor. Controlling the speed of a DC motor is very important as any small change can lead to instability of the closed loop system. The aim of this thesis is to show how DC motor can be controlled by using PID controller in MATLAB. The development of the PID controller with the mathematical model of DC motor is done using automatic tuning method. The PID parameter is to be test with an actual motor also with the PID controller in MATLAB Simulink. In this paper describe the results to demonstrate the effectiveness and the proposed of this PID controller produce significant improvement control performance and advantages of the control system DC motor. Mrs Khin Ei Ei Khine | Mrs Win Mote Mote Htwe | Mrs Yin Yin Mon ""Simulation DC Motor Speed Control System by using PID Controller"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25114.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/25114/simulation-dc-motor-speed-control-system-by-using-pid-controller/mrs-khin-ei-ei-khine
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).
Speed controller design for three-phase induction motor based on dynamic ad...IJECEIAES
Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.
Internal Model Based Vector Control of Induction MotorIJMER
This paper deals with the design of PID and Internal Model Controllers (IMC) in adjusting
the speed of induction machine under disturbances and set point changes. The performance of PID
controller is compared with IMC. The internal model control is an alternative to the classic feedback
structure. Internal model control is composed of an inverse model connected in series with the plant and a
forward model connected in parallel with the plant, this structure allows the error feedback to reflect the
effect of disturbances and plant mismodelling resulting in a robust control loop. The IMC provides good
performance and robustness against the disturbances in system when compared with the PID controller. A
simulation study of these methods is presented using MATLAB/SIMULINK.
This document compares the switching behaviors of field oriented control (FOC) and direct torque control (DTC) for induction motors. Experimental tests using a dSpace 1103 controller board show that under no load conditions, FOC produces less torque ripple than DTC. However, the switching frequency of the inverter for a FOC controlled motor is about 75% higher than for a DTC controlled motor. Therefore, DTC may be preferable when fast dynamic performance is critical, while FOC provides better torque quality.
Due to extensive use of motion control system in industry, there has been growing research on proportional-integral-derivative (PID) controllers. DC motors are widely used various areas of industrial applications. The aim of this paper is to implement efficient method for controlling speed of DC motor using a PID controller based. Proposed system is implemented using arduino microcontroller and PID controller. Motor speed is controlled through PID based revolutions per minute of the motor. This encoder data will be send through microcontroller to Personal Computer with PID controller implemented in MATLAB. Results shows that PID controllers used provide efficient controlling of DC motor.
This document discusses tuning the parameters of a PID controller for a separately excited DC motor drive system using genetic algorithms. It first describes the DC motor model and simulink modeling. It then provides background on PID controllers and genetic algorithms. The document proposes using genetic algorithms to tune the PID controller parameters (KP, KI, KD) to minimize errors and improve the step response of the controlled DC motor system. It presents the tuning methodology, comparing conventional PID tuning to GA-based tuning. Simulation results are presented and discussed, showing that GA tuning improves the step response by reducing overshoot and settling time compared to the conventional PID controller.
Speed Control of Dc Motor using Adaptive PID with SMC SchemeIRJET Journal
This document summarizes research on using an adaptive PID controller with sliding mode control (SMC) to control the speed of a DC motor. It begins by introducing DC motors and their use in industry. It then discusses PID, SMC, and adaptive PID+SMC control approaches. The document outlines the modeling of a separately excited DC motor. It presents the design of PID, SMC, and adaptive PID+SMC controllers for speed control. Simulation results using MATLAB/Simulink show that the adaptive PID+SMC controller provides superior performance to PID and SMC alone in terms of settling time, rise time, and eliminating overshoot under parameter variations and disturbances.
This document analyzes the closed-loop speed control of a chopper-fed separately excited DC motor using PI controllers. It presents the modeling of a separately excited DC motor and discusses various controller types for DC motor speed control. The proposed system uses a buck converter/chopper to control the armature voltage and thereby the speed of the DC motor. PI controllers are used to generate PWM pulses for the chopper by comparing the reference and feedback speeds. Simulation and experimental results are presented to validate the closed-loop speed control using PI controllers for different load conditions.
The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
Speed Control of DC Motor using PID FUZZY Controller.Binod kafle
speed control of separately excited dc motor using fuzzy PID controller(FLC).In this research, speed of separately excited DC motor is controlled at 1500 RPM using two approaches i.e. PSO PID and fuzzy logic based PID controller. A mathematical model of system is needed for PSO PID while knowledge based rules obtained via experiment required for fuzzy PID controller . The conventional PID controller parameters are obtained using PSO optimization technique. The simulation is performed using the in-built toolbox from MATLAB and output response are analyzed. The tuning of fuzzy PID uses simple approach based on the rules proposed and membership function of the fuzzy variables. Design specification of fuzzy logic controller (FLC) requires fuzzification, rule list and defuzzification process. The FLC has two input and three output. Inputs are the speed error and rate of change in speed error. The corresponding outputs are Kp, Ki and Kd. There are 25 fuzzy based rule list. FLC uses mamdani system which employs fuzzy sets in consequent part. The obtained result is compared on the basis of rise time, peak time, settling time, overshoot and steady state error. PSO PID controller has fast response but slightly greater overshoot whereas fuzzy PID controller has sluggish response but low overshoot. The selection can be done on the basis of system properties and working environment conditions. PSO PID can be used where the response desired is fast like robotics where as fuzzy PID can be used where desired operation is smooth like industries.
Fuzzy controlled dtc fed by a four switch inverter for induction motoreSAT Journals
Abstract
Direct Torque Control of induction motor fed drives has become popular and widely used in industries due to fast and good
torque response. Induction motors (IM) are simple in construction and are less sensitive to the motor parameters compared to
other vector control methods. The conventional DTC is based on flux and torque hysteresis controllers. Induction motor is fed
from a Four Switch Inverter generating the voltage vectors of the Six Switch Inverter by reconfiguration. Applying the most
optimized voltage vector that produce fastest dynamic torque response during transient states. Fuzzy logic concept is a most
efficient artificial integilence method which has high application in electric motor drives. A method to achieve fastest dynamic
performance by modifying the two leg inverter fed DTC of induction motor based on Fuzzy Logic Concept is used here. This paper
presents a rule-based fuzzy logic controller scheme designed and applied for the speed control of an induction motor fed from a
four switch three phase inverter emulating the six switch three phase inverter. Due to the usage of the Fuzzy logic concept, the
reliability, efficiency and performance of ac drive increases. Initial torque peak and torque ripple are minimized in the four switch
three phase inverter based DTC using Fuzzy Logic.
Key Words: Direct Torque Control , Four Switch/Six Switch Three Phase Inverter, Fuzzy Logic, Induction motor(IM).
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
This document compares different fuzzy logic controllers for improving the dynamic response of an indirect vector controlled induction motor drive. It presents a new fuzzy PI controller with scaling factors and evaluates its performance against fuzzy PI and fuzzy MRAC (model reference adaptive control) controllers. Simulation results show that the proposed fuzzy PI with scaling factors has a faster settling time than fuzzy PI, and is less complex than fuzzy MRAC while still providing good parameter insensitivity. The proposed controller provides a compromise between complexity, accuracy and settling time for induction motor applications.
Speed Control of Induction Motor using FOC MethodIJERA Editor
This document presents a method for speed control of induction motors using field oriented control (FOC). FOC works by controlling the flux and torque producing currents in the motor separately. It describes the FOC algorithm which involves transforming currents between stationary and rotating reference frames to allow proportional-integral control of flux and torque. Simulation results show that a PI controller provides fast, accurate speed control of an induction motor model across its full operating range with minimal overshoot. FOC allows induction motors to operate smoothly at variable speeds like DC motors and provides benefits like reduced motor size and cost.
Speed Control of DC Motor using PID Controller for Industrial Applicationijtsrd
This paper is to design PID controller to supervise and control the speed response of the DC motor and MATLAB program is used for industrial application . PID controllers are widely used in a industrial plants because of their simplicity and robustness. The results obtained from simulation are approximately similar to that obtained by practical. Also the dynamic behavior is studied. Manoj Kumar Ranwa | Ameen Uddin Ahmad "Speed Control of DC Motor using PID Controller for Industrial Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26599.pdfPaper URL: https://www.ijtsrd.com/engineering/electrical-engineering/26599/speed-control-of-dc-motor-using-pid-controller-for-industrial-application/manoj-kumar-ranwa
Speed control of dc motor using relay feedback tuned piAlexander Decker
This document discusses speed control of a DC motor using different controller types, including a relay feedback tuned PI controller, fuzzy PI controller (FPIC), and self-tuned fuzzy PI controller (STFPIC). The FPIC and STFPIC are developed using fuzzy logic to overcome limitations of conventional PID controllers for nonlinear systems without an accurate mathematical model. An experimental setup is used to test the controllers' performance on a DC motor. Results show the model-independent STFPIC and FPIC controllers improve speed control performance compared to the relay-tuned PI controller.
TORQUE CONTROL OF AC MOTOR WITH FOPID CONTROLLER BASED ON FUZZY NEURAL ALGORITHMijics
Nowadays in the complicated systems, design of proper and implementable controller has a most importance. With respect to ability of fractional order systems in complicated systems identification as a first order fractional system with time delay, usage of fractional order PID has a proper result. From one side flexibility of fractional calculus than integer order has been topics of interest to the researchers. From another side, PMSM motors which are one the AC motor types, has been allocated largely accounted position in industry and used in variety applications. Therefore in this paper torque direct control of PMSM motors with FOPID based on model is proposed. Also fuzzy neural controllers are widely considered. Reason of this is success of fuzzy neural controller in control and identification of uncertain and complicated systems. The proposed method in this paper is combination of FOPID controller with fuzzy neural supervision system which with coefficients setting of this controller, control operation of PMSM will improve. Results of proposed method show the ability of proposed technique in reference signal tracking, elimination of disturbances effects and functional robustness in presence of noise and uncertainty. The results show the error averagely in three condition, nominal form, step disturbance and noise and uncertainly will decrease 11.66% in proposed method (FNFOPID) with Integral Square Error criterion and 7.69% with Integral Absolute Error criterion in comparison to FOPID.
A New Induction Motor Adaptive Robust Vector Control based on Backstepping IJECEIAES
In this paper, a novel approach to nonlinear control of induction machine, recursive on-line estimation of rotor time constant and load torque are developed. The proposed strategy combines Integrated Backstepping and Indirect Field Oriented Controls. The proposed approach is used to design controllers for the rotor flux and speed, estimate the values of rotor time constant and load torque and track their changes on-line. An open loop estimator is used to estimate the rotor flux. Simulation results are presented which demonstrate the effectiveness of the control technique and on-line estimation.
This paper introduces experimental comparison study between six and four switch inverter fed three phase induction motor drive system. The control strategy of the drive is based on speed sensoreless vector control using model reference adaptive system as a speed estimator. The adaptive mechanism of speed control loop depends on fuzzy logic control. Four switch inverter conFigureurations reduces the cost of the inverter, the switching losses, the complexity of the control algorithms, interface circuits, the computation of real-time implementation, volume-compactness and reliability of the drive system. The robustness of the proposed model reference adaptive system based on four switch three-phase inverter (FSTPI) fed induction motor drive is verified experimentally at different operating conditions. Experimental work is carried using digital signal processor (DSP1103) for a 1.1 kW motor. A performance comparison of the proposed FSTP inverter fed IM drive with a conventional six switch three-phase inverter (SSTP) inverter system is also made in terms of speed response. The results show that the proposed drive system provides a fast speed response and good disturbance rejection capability. The proposed FSTP inverter fed IM drive is found quite acceptable considering its performance, cost reduction and other advantages features.
Simulation DC Motor Speed Control System by using PID Controllerijtsrd
Speed control system is the most common control algorithm used in industry and has been universally accepted in industrial control. One of the applications used here is to control the speed of the DC motor. Controlling the speed of a DC motor is very important as any small change can lead to instability of the closed loop system. The aim of this thesis is to show how DC motor can be controlled by using PID controller in MATLAB. The development of the PID controller with the mathematical model of DC motor is done using automatic tuning method. The PID parameter is to be test with an actual motor also with the PID controller in MATLAB Simulink. In this paper describe the results to demonstrate the effectiveness and the proposed of this PID controller produce significant improvement control performance and advantages of the control system DC motor. Mrs Khin Ei Ei Khine | Mrs Win Mote Mote Htwe | Mrs Yin Yin Mon ""Simulation DC Motor Speed Control System by using PID Controller"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25114.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/25114/simulation-dc-motor-speed-control-system-by-using-pid-controller/mrs-khin-ei-ei-khine
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).
Speed controller design for three-phase induction motor based on dynamic ad...IJECEIAES
Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.
Internal Model Based Vector Control of Induction MotorIJMER
This paper deals with the design of PID and Internal Model Controllers (IMC) in adjusting
the speed of induction machine under disturbances and set point changes. The performance of PID
controller is compared with IMC. The internal model control is an alternative to the classic feedback
structure. Internal model control is composed of an inverse model connected in series with the plant and a
forward model connected in parallel with the plant, this structure allows the error feedback to reflect the
effect of disturbances and plant mismodelling resulting in a robust control loop. The IMC provides good
performance and robustness against the disturbances in system when compared with the PID controller. A
simulation study of these methods is presented using MATLAB/SIMULINK.
This document compares the switching behaviors of field oriented control (FOC) and direct torque control (DTC) for induction motors. Experimental tests using a dSpace 1103 controller board show that under no load conditions, FOC produces less torque ripple than DTC. However, the switching frequency of the inverter for a FOC controlled motor is about 75% higher than for a DTC controlled motor. Therefore, DTC may be preferable when fast dynamic performance is critical, while FOC provides better torque quality.
Due to extensive use of motion control system in industry, there has been growing research on proportional-integral-derivative (PID) controllers. DC motors are widely used various areas of industrial applications. The aim of this paper is to implement efficient method for controlling speed of DC motor using a PID controller based. Proposed system is implemented using arduino microcontroller and PID controller. Motor speed is controlled through PID based revolutions per minute of the motor. This encoder data will be send through microcontroller to Personal Computer with PID controller implemented in MATLAB. Results shows that PID controllers used provide efficient controlling of DC motor.
This document discusses tuning the parameters of a PID controller for a separately excited DC motor drive system using genetic algorithms. It first describes the DC motor model and simulink modeling. It then provides background on PID controllers and genetic algorithms. The document proposes using genetic algorithms to tune the PID controller parameters (KP, KI, KD) to minimize errors and improve the step response of the controlled DC motor system. It presents the tuning methodology, comparing conventional PID tuning to GA-based tuning. Simulation results are presented and discussed, showing that GA tuning improves the step response by reducing overshoot and settling time compared to the conventional PID controller.
Speed Control of Dc Motor using Adaptive PID with SMC SchemeIRJET Journal
This document summarizes research on using an adaptive PID controller with sliding mode control (SMC) to control the speed of a DC motor. It begins by introducing DC motors and their use in industry. It then discusses PID, SMC, and adaptive PID+SMC control approaches. The document outlines the modeling of a separately excited DC motor. It presents the design of PID, SMC, and adaptive PID+SMC controllers for speed control. Simulation results using MATLAB/Simulink show that the adaptive PID+SMC controller provides superior performance to PID and SMC alone in terms of settling time, rise time, and eliminating overshoot under parameter variations and disturbances.
This document analyzes the closed-loop speed control of a chopper-fed separately excited DC motor using PI controllers. It presents the modeling of a separately excited DC motor and discusses various controller types for DC motor speed control. The proposed system uses a buck converter/chopper to control the armature voltage and thereby the speed of the DC motor. PI controllers are used to generate PWM pulses for the chopper by comparing the reference and feedback speeds. Simulation and experimental results are presented to validate the closed-loop speed control using PI controllers for different load conditions.
The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
Speed Control of DC Motor using PID FUZZY Controller.Binod kafle
speed control of separately excited dc motor using fuzzy PID controller(FLC).In this research, speed of separately excited DC motor is controlled at 1500 RPM using two approaches i.e. PSO PID and fuzzy logic based PID controller. A mathematical model of system is needed for PSO PID while knowledge based rules obtained via experiment required for fuzzy PID controller . The conventional PID controller parameters are obtained using PSO optimization technique. The simulation is performed using the in-built toolbox from MATLAB and output response are analyzed. The tuning of fuzzy PID uses simple approach based on the rules proposed and membership function of the fuzzy variables. Design specification of fuzzy logic controller (FLC) requires fuzzification, rule list and defuzzification process. The FLC has two input and three output. Inputs are the speed error and rate of change in speed error. The corresponding outputs are Kp, Ki and Kd. There are 25 fuzzy based rule list. FLC uses mamdani system which employs fuzzy sets in consequent part. The obtained result is compared on the basis of rise time, peak time, settling time, overshoot and steady state error. PSO PID controller has fast response but slightly greater overshoot whereas fuzzy PID controller has sluggish response but low overshoot. The selection can be done on the basis of system properties and working environment conditions. PSO PID can be used where the response desired is fast like robotics where as fuzzy PID can be used where desired operation is smooth like industries.
Fuzzy controlled dtc fed by a four switch inverter for induction motoreSAT Journals
Abstract
Direct Torque Control of induction motor fed drives has become popular and widely used in industries due to fast and good
torque response. Induction motors (IM) are simple in construction and are less sensitive to the motor parameters compared to
other vector control methods. The conventional DTC is based on flux and torque hysteresis controllers. Induction motor is fed
from a Four Switch Inverter generating the voltage vectors of the Six Switch Inverter by reconfiguration. Applying the most
optimized voltage vector that produce fastest dynamic torque response during transient states. Fuzzy logic concept is a most
efficient artificial integilence method which has high application in electric motor drives. A method to achieve fastest dynamic
performance by modifying the two leg inverter fed DTC of induction motor based on Fuzzy Logic Concept is used here. This paper
presents a rule-based fuzzy logic controller scheme designed and applied for the speed control of an induction motor fed from a
four switch three phase inverter emulating the six switch three phase inverter. Due to the usage of the Fuzzy logic concept, the
reliability, efficiency and performance of ac drive increases. Initial torque peak and torque ripple are minimized in the four switch
three phase inverter based DTC using Fuzzy Logic.
Key Words: Direct Torque Control , Four Switch/Six Switch Three Phase Inverter, Fuzzy Logic, Induction motor(IM).
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
This document compares different fuzzy logic controllers for improving the dynamic response of an indirect vector controlled induction motor drive. It presents a new fuzzy PI controller with scaling factors and evaluates its performance against fuzzy PI and fuzzy MRAC (model reference adaptive control) controllers. Simulation results show that the proposed fuzzy PI with scaling factors has a faster settling time than fuzzy PI, and is less complex than fuzzy MRAC while still providing good parameter insensitivity. The proposed controller provides a compromise between complexity, accuracy and settling time for induction motor applications.
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effect at front pile and rear pile severally, this phenomenon is called multiple soil arching effect; the residual
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continuous increase of double row pile spacing.At the same time the soil arching effect of rear pile decreases,
while the soil arching effect of front pile increases and finally the soil arching effect between front pile and rear
pile will be equal.
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6. performance analysis of pd, pid controllers for speed control of dc motork srikanth
Aim of this paper different Proportional-Integral- Derivative (PID) controller fine-tuning techniques are investigated for speed control of DC motor. At the start PID controller parameters for different tuning techniques are involved and then applied to the DC motor model for motion (speed) control. Simulation results are display, using these controllers, objective of this paper, the performance of a choose dc motor controlled by a proportional-integral-derivative (PID) controller is below the similar transient conditions and performances are compared.
This document describes the design of a PI controller to minimize speed error for a DC servo motor. It presents a mathematical model of the DC servo motor and designs a PI controller using Simulink. The PI controller gains are adjusted to minimize overshoot, rise time, settling time, and speed error when the reference input changes between 110V to 220V and 110V to 55V. Simulation results show the PI controller is effective at maintaining near zero speed error and improving transient response.
In this paper, we first write a description of the operation of DC motors taking into account which parameters the speed depends on thereof. The PID (Proportional-Integral-Derivative) controllers are then briefly described, and then applied to the motor speed control already described , that is, as an electronic controller (PID), which is often referred to as a DC motor. The closed loop speed control of a Brush DC motor is developed applying the well-known PID control algorithm. The objective of this work is to designed and simulate a new control system to keep the speed of the DC motor constant before variations of the load (disturbances), automatically depending to the PID controller. The system was designed and implementation by using MATLAB/SIMULINK and DC motor.
This document presents a method for tuning the parameters of a PID controller for a brushless DC motor using particle swarm optimization. PSO is used to find the optimal proportional, integral and derivative gains to minimize error metrics for the motor's step response. The BLDC motor is modeled in Simulink. PSO searches through potential solutions in a multi-dimensional space to determine PID parameters that produce the best step response with minimal overshoot, rise time, settling time and steady state error. The results show the PSO-tuned PID controller achieves better dynamic performance than other methods.
Performance analysis of Fuzzy logic based speed control of DC motorIOSR Journals
The document proposes a fuzzy logic controller to improve the speed control of a separately excited DC motor compared to a conventional PID controller. It designs fuzzy logic membership functions and rules to self-tune the parameters of a PID controller based on motor speed error and change in error. Simulation results show the fuzzy tuned PID controller achieves better dynamic and static response than a conventional PID, with less overshoot, shorter settling time, and smaller steady state error.
IRJET-Investigation of Three Phase Induction Speed Control Strategies using N...IRJET Journal
This document discusses speed control strategies for a three-phase induction motor using different controllers. It first introduces induction motors and their challenges in speed control due to their complex nonlinear dynamics. It then discusses various speed control techniques including proportional (P), proportional-integral (PI) and proportional-integral-derivative (PID) control. The document presents MATLAB simulations of speed control using these different controllers under full load conditions. The results show that PID control provides more efficient speed control with minimal steady state error and faster response compared to no controller or only P control. Vector control techniques are used to implement the speed controllers in the simulation.
Modeling & Simulation of PMSM Drives with Fuzzy Logic ControllerIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Speed Control and Parameter Variation of Induction Motor Drives using Fuzzy L...IRJET Journal
This document compares the speed control of an induction motor using auto-disturbance rejection controllers (ADRC) and fuzzy logic controllers. It presents the mathematical models and control schemes for both approaches. Simulation results show that the fuzzy logic controller has better performance characteristics than the ADRC controllers in responding to load disturbances, motor parameter variations, and model uncertainty. The fuzzy logic controller is able to settle to the reference speed faster after a disturbance and has a lower steady state error.
This document presents a study comparing three methods - PID, LQR, and feedforward control - for controlling the speed of a DC motor in a washing machine with random loads. The motor's actual parameters were measured and implemented in MATLAB simulations. Results show the LQR control method provided the best performance, with the smallest transient period and smallest change in output in response to disturbances, allowing for more consistent motor speed despite changing loads compared to PID and feedforward control.
This document summarizes a journal article that proposes a fuzzy logic approach for sensorless vector control of an induction motor using an efficiency optimization technique. It presents the following:
1) A dynamic model and state space model of the induction motor in a synchronous reference frame for vector control without sensors.
2) A fuzzy logic based online efficiency optimization controller that interfaces with the drive system to minimize power consumption.
3) The controller decrements the flux in steps until the measured input power is minimized. Membership functions and rules for the fuzzy controller are provided.
4) Performance of the drive is analyzed with and without the fuzzy controller using MATLAB/Simulink simulations. The fuzzy approach is found to improve efficiency
Modeling of DC Motor and Choosing the Best Gains for PID Controllerijtsrd
This document summarizes research on modeling a DC motor and using a PID controller to control its position. The researchers modeled the motor mathematically in two ways: by estimating parameters like resistance, inductance, torque constant, and back EMF constant; and based on the motor's first-order speed response curve. Both methods were used to derive transfer functions relating the motor's input voltage to its speed and position. Ziegler-Nichols tuning methods and MATLAB simulations were used to select PID gains for position control. Results showed the PID controller could accurately control the motor's angle based on feedback from an optical encoder. The controlled DC motor could then be applied to positions systems like a robot arm.
Modified Chattering Free Sliding Mode Control of DC MotorIJMER
This document discusses the design of a modified sliding mode controller for position control of a DC motor. It begins with an introduction to PID controllers and sliding mode controllers. It then provides the mathematical modeling of a DC motor and describes the conventional sliding mode control approach. It discusses some issues with chattering in sliding mode control and proposes a modified approach using a boundary layer. The document also describes tuning a PID controller using the Ziegler-Nichols method. It aims to compare the performance of the modified sliding mode controller to PID controllers for position control of a DC motor.
A review paper on closed loop control of bldc motor using fuzzy logicIRJET Journal
This document reviews a closed loop control method for a brushless DC motor (BLDC) using a fuzzy logic controller. It describes using a single current sensor on the DC link to estimate phase currents and avoid the cost of multiple sensors. The proposed system was simulated in MATLAB/Simulink using PI and fuzzy logic speed controllers. The simulation results showed that the fuzzy logic controller provided better motor speed control performance compared to the PI controller.
IRJET-Comparison between Scalar & Vector Control Technique for Induction Moto...IRJET Journal
This document provides a comparison of scalar and vector control techniques for controlling the speed of three-phase induction motors. It presents the modeling and simulation of scalar and vector control methods for an induction motor drive using a two-level inverter in MATLAB/Simulink. The scalar control technique is simpler to implement but provides lower performance compared to the vector control technique. Simulation results show that vector control reduces torque ripple and lower order harmonics compared to scalar control, providing better speed regulation and power quality. Vector control also offers faster dynamic response making it suitable for applications with frequent load changes.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This document summarizes a research paper that analyzes speed control of a permanent magnet brushless DC motor using fuzzy logic techniques. It begins with an introduction to brushless DC motors and their applications. It then describes the dynamics and modeling of a brushless DC motor drive system. Next, it discusses conventional PID speed control and introduces fuzzy logic control as an alternative method. The next sections provide details on fuzzy logic controller design and simulations comparing the performance of PID and fuzzy logic controllers under different operating conditions. The conclusions indicate that the fuzzy logic controller provides better damping and disturbance rejection compared to the PID controller.
IRJET- Vector Control of Three Phase Induction MotorIRJET Journal
This document discusses vector control of a three-phase induction motor. Vector control, also called field-oriented control, allows independent control of torque and flux in induction motors, similar to DC motors. The document describes:
1) How vector control works by transforming stator currents into orthogonal d-q components representing flux and torque.
2) The principle of field-oriented control which locks the d-q reference frame to the rotor flux vector for decoupled control of flux and torque.
3) The simulation model built in MATLAB/Simulink to test vector control, including blocks for Clarke/Park transformations, current control, and a PI speed controller.
Direct torque control of induction motor using space vector modulationIAEME Publication
This document discusses direct torque control of an induction motor using space vector modulation. It begins with introducing direct torque control as an alternative to field oriented control for controlling torque and flux directly and independently. It then provides details on the principles of vector control and direct torque control in stator reference frames. The document describes the modeling of an induction motor and simulations performed in MATLAB to validate the direct torque control approach. The simulations demonstrate control of speed, torque, and flux under different gain settings of the PI controller.
This document summarizes a study that uses Takagi-Sugeno fuzzy logic control as a speed controller for indirect field oriented control of an induction motor drive. The study builds a simulation model of indirect field oriented control for an induction motor in MATLAB Simulink. Takagi-Sugeno fuzzy logic is then applied as the speed controller using error and derivative error as inputs and change of torque command as the output. Simulation results show zero overshoot, a rise time of 0.4 seconds, and a settling time of 0.4 seconds. The steady state error is 0.01% indicating the speed can accurately follow the reference speed. The fuzzy logic controller provides effective speed control for the induction motor drive.
Speed Control of a Seperately Excited DC Motor by Implementing Fuzzy Logic Co...IRJET Journal
This document describes the use of a fuzzy logic controller to control the speed of a separately excited DC motor. A mathematical model of the DC motor is presented. A PID controller is also designed for speed control and its performance is compared to the fuzzy logic controller. A fuzzy logic controller with two inputs - speed error and change in speed error - and seven fuzzy rules is developed. Simulation results in MATLAB/Simulink show that the fuzzy logic controller has better performance than the PID controller in terms of overshoot, settling time, and steady state error for speed control of the separately excited DC motor.
This document compares the performance of three different rule-based fuzzy logic controllers (FLCs) with 49, 25, and 9 rules for controlling the speed of a permanent magnet synchronous motor (PMSM) drive system. Simulation results show that a FLC with more rules (49 rules) provides superior performance in terms of speed tracking accuracy and settling time compared to FLCs with fewer rules (25 and 9 rules), but requires more computational resources. The performance decreases as the number of rules in the FLC decreases. Thus, there is a tradeoff between control performance and computational complexity depending on the number of rules used in the FLC for PMSM speed control applications.
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Speed Control of Induction Motor by Using Intelligence Techniques
1. S. Riaz Ahamed et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 1( Part 5), January 2015, pp.130-135
www.ijera.com 130 | P a g e
Speed Control of Induction Motor by Using Intelligence
Techniques
S. Riaz Ahamed*
, J. N. Chandra Sekhar**
, Dinakara Prasad Reddy P***
Department of EEE, Sri Venkateswara University College of Engineering, Tirupati.
ABSTRACT
This paper gives the comparative study among various techniques used to control the speed of three phase
induction motor. In this paper, indirect vector method is used to control the speed of Induction motor. Firstly
Simulink Model is developed by using MATLAB/ Simulink software. PI controller, Fuzzy PI Hybrid controller,
Genetic Algorithm (GA) are the techniques involved in control Induction motor and the results are compared.
By converting three phase supply currents coming from stator to Flux and Torque components of current the
speed responses such as rise time, overshoot, settling time and speed regulation at load have been observed and
compared among the techniques. The PI controller parameters defined by an objective function are calculated by
using Genetic Algorithms presented good performance compared to Fuzzy PI Hybrid controller which has
parameters chosen by the human operator.
Keywords—Induction Motor, PI Controller, Fuzzy PI Hybrid Controller, Indirect Vector control, Genetic
Algorithm
I. Introduction
Now a day’s Induction motors are the work
horses of many industries which also replaced DC
machines with their various advantages like lack of
commutator, lower cost, reduced maintenance cost,
robust, less weight and rugged structure. Because of
their complex characteristics, it is not easier to
control the speed of Induction motor like DC motor,
so the vector control is used. It is introduced by
Blaschke and Hasse has resulted in remarkable
change in the field of electrical drives. Indirect vector
control is used in this paper which is one of the types
of vector control. It is very popular form of control of
Induction motor because this control strategy can
provide the same performance as achieved from a
separately excited DC Motor.
The simple structure and its good performance
has made the PI controller the best controller in the
industry. Its functions depends on two parameters
namely proportional gain Kp and Integral gain Ki.
Several methods can be used to tune PI controller.
The Fuzzy set theory, introduced by L.Zadeh is the
mathematical tool for Fuzzy Logic Controller (FLC).
It can be used in control of Induction Motor because
of its advantages such as it does not need a
mathematical model for the system, it is just based on
linguistic rules with IF-THEN general structure
which is based on human logic.
Methods such as Pole assignment method and
Ziegler-Nichols method have major inconvenience as
it is necessary to have prior knowledge of various
parameters of the Induction motor. An optimization
procedure can be developed to design the good
controller. Genetic Algorithm has been employed
successfully to solve the complex optimization
problems. The parameters of different controllers can
be determined by using Genetic Algorithm due to
their reasonable accuracy and fast convergence.
The PI controller parameters are determined by
minimizing Objective Function. The goal of this
work is to show that Optimization can be achieved by
optimization of PI Controller parameters. This can be
observed by comparing the results of Genetic
Algorithm based PI controller with PI, Fuzzy PI
Hybrid Controller.
II. Dynamics Of Induction Motor
The Squirrel cage Induction Motor using the
Direct axis and Quadrature axis (d-q) theory in the
stationary reference frame [1-2] shown in the figures
below needs less variables and analysis becomes
easy.
Fig. 1. Stator and rotor axis in two axis reference
frame (a)q-axis and (b)d-axis
RESEARCH ARTICLE OPEN ACCESS
2. S. Riaz Ahamed et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 1( Part 5), January 2015, pp.130-135
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A. Electrical system of Induction Motor
𝑉𝑑𝑠
𝑉𝑞𝑠
0
0
=
Rs + SLs −ωLs SLm −ωLm
ωLs Rs + SLs ωLm SLm
SLm − ω − ωr Lm Rr + SLr − ω − ωr Lr
ω − ωr Lm Lm 𝜕 ω − ωr Lr Rr + SLr
ids
iqs
idr
iqr
ϕqs
= Lsiqs + Lm iqr ϕds
= Lsids_ + Lm idr
ϕqr
= Lriqr + Lm iqs ϕdr
= Lridr + Lm ids
Ls = Lls + Lm Lr = Llr + Lm
Te =
3
2
P
2
ϕds
iqs − ϕqs
ids
A. Mechanical system of Induction Motor
d
dt
ωm =
1
2H
Te − Fωm − Tm
d
dt
θm = ωm
Where,
𝑅 𝑠 , 𝐿𝑙𝑠 : Resistanceand leakage inductance of
stator
𝑅 𝑟 , 𝐿𝑙𝑟 : Resistanceand leakage inductance of
rotor
𝐿 𝑚 : The magnetizing Inductance
Ls , Lr : Stator and rotor inductances
𝑉𝑞𝑠 , iqs : q axis component of stator voltage and
current
𝑉𝑞𝑟 , iqr :q axis component of rotor voltage and
current
𝑉𝑑𝑠 , ids :d axis component of stator voltage and
current
𝑉𝑑𝑟 , idr :d axis component of rotor voltage and
current
𝜙 𝑞𝑠 , ϕds
: q and d axis components of stator flux
𝜙 𝑞𝑟 , ϕdr
: q and d axis components of rotor flux
ωm : Angular velocity of rotor
θm : Angular position of rotor
P : Number of poles
p: Pairs of Poles (
𝑃
2
)
𝜔𝑟: Electrical angular velocity (𝜔𝑟. 𝑝)
𝜃𝑟: Electrical rotor angular position(𝜃 𝑚 . 𝑝)
Te: Electromagnetic Torque
Tm : Mechanical Torque on Shaft
J: Load Inertia Constant
F:Friction Coefficient
III. Indirect Vector Control
The block diagram shown below is the Indirect
Vector Control Technique. Two control loops will
control induction motor drive namely Internal Pulse
Width Modulation current control loop and External
Speed control loop [3].
Fig. 2. Block Diagram of Indirect Vector Control
Technique.
The indirect vector control method is essentially
the same as direct vector control, but the unit vector
signals (cos𝜃𝑒 and sin𝜃𝑒) are generated in feed
forward manner using the measured rotor speed 𝜔𝑟
and the slip speed𝜔 𝑠𝑙 . Indirect vector control is
widely used in industrial applications. Current-
Controlled PWM Inverter acts as three phase
sinusoidal current source to Induction motor. The
error between reference speed 𝜔∗
and speed 𝜔 is
given to speed controller which outputs the command
Torque Te
∗
.
A shown in the Block diagram above, Torque
and Rotor Flux can be independently controlled by q-
axis stator current iqs and d-axis stator current ids
respectively.
The q-axis Stator Current Reference 𝑖 𝑞𝑠
∗
is
calculated from Command Torque Signal Te
∗
as
shown in below equation.
iqs
∗
=
2
3
2
P
Lr
Lm
Te
ψr est
𝜓𝑟 𝑒𝑠𝑡 is the Estimated Rotor Flux Linkage. It can
be calculated by equation shown below.
ψr est
=
Lm ids
1 + τrs
Where τr =
Lr
Rr
is Time Constant of Rotor.
The d-axis stator current reference ids
∗
is
calculated from Rotor flux reference input 𝜓𝑟
∗
.
ids
∗
=
ψr
∗
Lm
The rotor flux position 𝜃𝑒 which is required for
coordinate transformation is calculated from slip
frequencyωsl and rotor speed 𝜔 as shown in equation
below.
θe = ωsl + ω dt
3. S. Riaz Ahamed et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 1( Part 5), January 2015, pp.130-135
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The slip frequency ωsl is calculated from stator
reference current iqs
∗
and motor parameters.
ωsl =
Lm
ψr est
Rr
Lr
iqs
∗
The current references iqs
∗
and ids
∗
are converted
into three phase currents𝑖 𝑎
∗
, 𝑖 𝑏
∗
, 𝑖 𝑐
∗
by using Park’s
Transformation for the current regulators. The current
regulators will use the reference currents and the
measured currents to form the inverter gating signals.
To provide a good dynamic response during
transient conditions, the speed controller should
maintain the motor speed equal to reference speed
input.
IV. Speed Controllers
As already mentioned, the input to the speed
controller is the speed error signal, which is
difference between the reference speed and actual
speed. In this paper, three types of controllers are
used. They are PI controller, PI-Fuzzy Hybrid
Controller, Genetic Algorithm based PI controller.
4.1 PI Controller
Fig. 3. Block Diagram of PI Controller
Command Torque is the output signal of
controller where Kp is the proportional gain and Ki is
the integral gain.
Te n =Te n−1 + Kp ∆e n + Kie(n)
If the gains of the controller exceed a certain
value, the variations in the command torque become
too high and will decrease stability of the system. To
overcome this problem, a limiter ahead of the PI
controller is used.
Te n+1 =
Temax → Te n+1 ≥ Temax
−Temax → Te n+1 ≤ −Temax
4.2 Fuzzy Logic Controller
Good Dynamic stability of induction motor is
achieved when it has a good performance under
transient stability conditions such as Sudden Load
application or sudden load removal and sudden
increase or decrease in speed. In PI controller, the
tuning parameters depend on the ratings of the motor
but Fuzzy logic Controller does not require any
model of the motor and can handle complex
nonlinearities.
The Fuzzy logic controller shown in the figure
has three functional blocks
Fig. 4. Fuzzy Logic Controller.
Fig.5.Input Membership Functions for error speed (e)
rate of change of error speed (de)
Fig.6. Output Membership Function of Fuzzy Logic
Controller.
Table 1. Rule Matrix for Fuzzy Logic Controller
Triangular Membership functions are used to
represent input and output variablessuch as NB –
Negative Big, NM – Negative Medium, NS –
Negative Small, ZE – Zero, PS – Positive Small, PM
– Positive Medium, PB – Positive Big. Here,
Membership functions should be normalized between
-1 to +1[4].
The Fuzzy Rules are represented using IF-THEN
form. MAX-MIN Inference algorithm and Center of
Gravity Defuzzification Approach is used to get
Crisp output from Fuzzy Logic Controller. The fuzzy
4. S. Riaz Ahamed et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 1( Part 5), January 2015, pp.130-135
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rules were designed based on the dynamic behavior
of the error signal.
4.3 PI Fuzzy Hybrid Controller
Fig. 7. Block Diagram of PI Fuzzy Hybrid Controller
This controller [3-5] has the advantages of both
PI and Fuzzy Logic controller. Fuzzy logic is used
for pre-compensation of reference speed, which
changes reference speed given to PI controller in
accordance to rotor speed as shown in figure above
[3].
4.4 Genetic Algorithm based PI Controller
The simplified dynamic Model of Induction
Motor drive [6-7] is represented by the block diagram
shown below.
Fig. 8.Block diagram of speed system controller
If TL =0, then the Transfer Function is,
G S =
Kp S + Ki
P
J
S2 +
f+Kp
Lr
S +
Ki
J
The characteristic Equation is given as follows
P S = S2
+
f + Kp P
J
S +
KiP
J
= 0
By the imposition of two poles complex
combined with real part negative,𝑆1,2 = 𝜌(−1 ± 𝑗),
we get the equations to find Kp , Ki values
Ki =
2Jρ2
P
Kp =
2ρJ − f
P
Where 𝜌 is a Positive Constant.
Genetic Algorithms have been used to solve
difficult problems with objective functions that do
not possess “nice” properties such as continuity,
differentiability, satisfaction of the Lipchitz condition
etc. An objective Function is developed by above
equations and minimized using Genetic Algorithm to
find Kp, Ki values.
Genetic Algorithm was first developed by John
Holland and his colleagues in 1975. It is a stochastic
global search method that mimics the process of
Natural Evolution. The Genetic Algorithm starts with
no knowledge of the correct solution and depends
entirely on responses from its environment and
evolution operators(i.e. Reproduction, Crossover and
Mutation) to arrive at the best solution.GA maintains
and manipulates a population of solutions and
implements a “Survival of the Fittest” strategy in
their search for better solutions. This provides an
implicit as well as explicit parallelism that allows for
the exploitation of several promising areas of the
solution space at the same time. By starting at several
independent points and searching in parallel, the
algorithm avoids local minima and convergence to
sub optimal solutions. In this way, GA has been
shown to be capable of locating high performance
areas in complex domains without experiencing the
difficulties associated with high dimensionality, as
may occur with gradient descent techniques or
methods that rely on derivative information.
Genetic Algorithm [8-11] mainly consists of
three stages: Selection, Crossover and Mutation. New
individuals were created by performing these
operations which may be better than their parents.
This algorithm is repeated for many generations and
finally stops when reaching individuals that represent
optimal solution to the problem.
Fig. 9. Genetic Algorithm Architecture
In every generation, the genetic operators are
applied to selected individuals from present
population in order to create new population.
Generally, the three main genetic operators of
reproduction, crossover and mutation are performed.
To apply these operators, different probabilities are
chosen so that speed of convergence can be
controlled.
Reproduction is creation of new population by
simply copying the selected individuals without
changing them. Also there is a probability of
selection from new population by already developed
solution. There are number of selection methods
available based on same principle i.e. giving large
probability selection for fitter chromosomes.
Once the selection process is completed,
crossover operation is initiated which swaps certain
parts of the two individuals in a bid to capture the
good parts of old population and create better new
5. S. Riaz Ahamed et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 1( Part 5), January 2015, pp.130-135
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ones. The crossover probability indicates how often
crossover is performed. Typically this operator is
applied at a probability range of 0.6 to 0.8. The
mutation operator plays a secondary role in the
evolution .It helps to keep diversity in the population
by discovering new or restoring lost genetic materials
by searching the neighborhood solution space.
Mutation occurs with a small probability rate of 0.1%
to 10% of the entire population.
Genetic Algorithm can be used to tune the gains
of PI Speed Controller as shown in figure below.
Fig.10. Structure of the technique of optimization of
the PI controller by GA
The Objective Function can be written as shown
below
Fitness = e2
t dt = ω∗
t − ω t
2
dt
t
0
t
o
The block of the objective function is used to
estimate the performances of the PI controller by
minimizing this function.
The genetic algorithm parameters chosen for the
tuning purpose are shown below.
Table 2. Parameters of Genetic Algorithm
GA Property Value
Population Size 60
Maximum no. of Generations 100
Crossover Probability 0.8
Mutation Probability 0.1
Tolerance 10−6
After giving the above parameters to GA, the PI
controller can be easily tuned and thus system
performance can be improved. The parameters of the
PI speed controller obtained according to the
procedure of optimization by the technique of the GA
are given as Kp = 11.3006, Ki = 0.5609.
V. Simulation Results
The Simulation results for Sudden Speed
variation, sudden application and removal load are
observed. Initially, motor is running 120rad/sec,
suddenly speed is changed to 160rad/sec at 0.2sec.
Here Rise time, Peak overshoot, settling time is
observed for all controllers. The Load Torque of
10N-m is applied suddenly to motor at 2sec and
removed at 2.5sec. Here speed regulation at load is
calculated for all controllers.
Fig.11. Speed Response with PI controller
Fig. 12. Speed Response with PI-Fuzzy Hybrid
Controller
Fig.13. Speed Response with GA based PI
Controller.
From the simulation results of speed responses,
the rise time, peak overshoot, settling time and speed
regulation are better with GA based PI controller
compared to PI and PI-Fuzzy Hybrid Controller.
Table.3. Parameters using different Controllers.
Controllers
Parameters
PI
PI-
Fuzzy
Hybrid
GA
based PI
Rise Time(sec) 0.231 0.225 0.219
Peak Overshoot(rad/sec) 165 160.2 160.05
Settling Time(sec) 1.5 0.225 0.222
Speed Regulation (%) 4.36 0.75 0.31
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ISSN : 2248-9622, Vol. 5, Issue 1( Part 5), January 2015, pp.130-135
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VI. Conclusion
In this paper Indirect Vector Control is used to
control the speed of Induction Motor. The simulation
is carried out using MATLAB/Simulink Software.
The GA based PI controller showed better
performance compared to PI and PI-Fuzzy Hybrid
Controllers in terms of Rise time, Peak overshoot and
Settling time as well as Speed Regulation.
Appendix: Induction Motor Specifications.
Rated power 1.5 kW
Voltage 220V
Frequency 50Hz
Rotor Type Squirrel Cage
Stator resistance(Rs) 4.85Ω
Rotor resistance(Rr) 3.805Ω
Stator inductance(Ls) 0.274H
Rotor inductance(Lr) 0.274H
Mutual inductance(Lm) 0.258H
Moment of inertia(J) 0.031kg-m2
Friction Coefficient(f) 0.00114Nm/rad
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