This paper analyzes the effects of the bilateral control parameters variation on the stability, the transparency and the accuracy, and on the operational force that is applied to DC motor and the master system. The bilateral controller is designed for rehabilitation process. PD controller is used to control the position tracking and a force gain controller is used to control the motor torque. DOB eliminate the internal disturbance and RTOB to estimate the joint torque without using sensors. The system consists of two manipulators, each manipulator has 1dof, master and slave teleoperation system, 4 control-architecture channel, DOB and reaction force observer. The master system is attached to human oberator. The slave system is attached to external load. The aim in this paper is to design the controller so that it requires less force to move the master manipulator and at the same time achieve high performance in position tracking.
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.
Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control of In...ijsrd.com
Because of the low maintenance and robustness induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy Sliding mode controller. The fuzzy-logic with sliding mode speed controller is employed in the outer loop. The performance of Fuzzy Logic control technique has been presented and analyzed in this work. The fuzzy logic controller is found to be a very useful technique to obtain a high performance speed control. The indirect vector controlled induction motor drive involves decoupling of the stator current in to torque and flux producing components. The analysis, design and simulation of the fuzzy logic controller for indirect vector control of induction motor are carried out based on fuzzy set theory. The model is carried out using Matlab/Simulink. The simulation results shows the superiority of fuzzy sliding mode controller in controlling three phase Induction motor with indirect vector control technique.
This paper analyzes the effects of the bilateral control parameters variation on the stability, the transparency and the accuracy, and on the operational force that is applied to DC motor and the master system. The bilateral controller is designed for rehabilitation process. PD controller is used to control the position tracking and a force gain controller is used to control the motor torque. DOB eliminate the internal disturbance and RTOB to estimate the joint torque without using sensors. The system consists of two manipulators, each manipulator has 1dof, master and slave teleoperation system, 4 control-architecture channel, DOB and reaction force observer. The master system is attached to human oberator. The slave system is attached to external load. The aim in this paper is to design the controller so that it requires less force to move the master manipulator and at the same time achieve high performance in position tracking.
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.
Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control of In...ijsrd.com
Because of the low maintenance and robustness induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy Sliding mode controller. The fuzzy-logic with sliding mode speed controller is employed in the outer loop. The performance of Fuzzy Logic control technique has been presented and analyzed in this work. The fuzzy logic controller is found to be a very useful technique to obtain a high performance speed control. The indirect vector controlled induction motor drive involves decoupling of the stator current in to torque and flux producing components. The analysis, design and simulation of the fuzzy logic controller for indirect vector control of induction motor are carried out based on fuzzy set theory. The model is carried out using Matlab/Simulink. The simulation results shows the superiority of fuzzy sliding mode controller in controlling three phase Induction motor with indirect vector control technique.
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.
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 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.
PI controller design for indirect vector controlled induction motorISA Interchange
Decoupling of the stator currents is important for smoother torque response of indirect vector controlled induction motors. Typically, feedforward decoupling is used to take care of current coupling that requires exact knowledge of motor parameters, additional circuitry and signal processing. In this paper, a method is proposed to design the regulating proportional-integral gains that minimize coupling without any requirement of the additional decoupler. The variation of the coupling terms for change in load torque is considered as the performance measure. An iterative linear matrix inequality based H∞ control design approach is used to obtain the controller gains. A comparison between the feedforward and the proposed decoupling schemes is presented through simulation and experimental results. The results show that the proposed scheme is simple yet effective even without additional block or burden on signal processing.
Dynamic Simulation of Induction Motor Drive using Neuro Controlleridescitation
Induction Motors are widely used in Industries, because of the low maintenance
and robustness. Speed Control of Induction motor can be obtained by maximum torque and
efficiency. Apart from other techniques Artificial Intelligence (AI) techniques, particularly
the neural networks, improves the performance & operation of induction motor drives. This
paper presents dynamic simulation of induction motor drive using neuro controller. The
integrated environment allows users to compare simulation results between conventional,
Fuzzy and Neural Network controller (NNW).The performance of fuzzy logic and artificial
neural network based controller's are compared with that of the conventional proportional
integral controller. The dynamic Modeling and Simulation of Induction motor is done using
MATLAB/SIMULINK and the dynamic performance of induction motor drive has been
analyzed for artificial intelligent controller.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This paper presents an analysis by which the dynamic performances of a permanent magnet
brushless dc (PMBLDC) motor drive with different speed controllers can be successfully predicted. The
control structure of the proposed drive system is described. The dynamics of the drive system with a
classical proportional-integral-derivative (PID) and Fuzzy-Logic (FL) speed controllers are presented.
The simulation results for different parameters and operation modes of the drive system are investigated
and compared. The results with FL speed controller show improvement in transient response of the
PMBLDC drive over conventional PID controller. Moreover, useful conclusions stemmed from such a
study which is thought of good use and valuable for users of these controllers
Design of Fuzzy Logic Controller for Speed Regulation of BLDC motor using MATLABijsrd.com
Brushless DC (BLDC) motors drives are one of the electrical drives that are rapidly gaining popularity, due to their high efficiency, good dynamic response and low maintenance. The design and development of a BLDC motor drive for commercial applications is presented. The aim of paper is to design a simulation model of inverter fed PMBLDC motor with Fuzzy logic controller. Fuzzy logic controller is developed using fuzzy logic tool box which is available in Matlab. FIS editor used to create .FIS file which contains the Fuzzy Logic Membership function and Rule base. And membership functions of desired output. After creating .FIS file it is implemented in the Matlab Simulink. And the BLDC motor is run satisfactorily using the Fuzzy logic controller.
Power optimisation scheme of induction motor using FLC for electric vehicleAsoka Technologies
In electric vehicles (EVs) and hybrid EVs, energy efficiency is essential where the energy storage is limited. Adding to its high stability and low cost, the induction motor efficiency improves with loss minimisation. Also, it can consume more power than the actual need to perform its working when it is operating in less than full load condition. This study proposes a control strategy based on the fuzzy logic control (FLC) for EV applications. FLC controller can improve the starting current amplitude and saves more power. Through the MATLAB/SIMULINK software package, the performance of this control was verified through simulation. As compared with the conventional proportional integral derivative controller, the simulation schemes show good, high-performance results in time-domain response and rapid rejection of system-affected disturbance. Therefore, the core losses of the induction motor are greatly reduced, and in this way improves the efficiency of the driving system. Finally, the suggested control system is validated by the experimental results obtained in the authors’ laboratory, which are in good agreement with the simulation results.
Speed Control of Induction Motor by Using Intelligence TechniquesIJERA Editor
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.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
Speed Control of Brushless Dc Motor Using Fuzzy Logic Controlleriosrjce
This paper presents a control scheme of a fuzzy logic for the brushless direct current (BLDC)
permanent magnet motor drives. The mathematical model of BLDC motor and fuzzy logic algorithm is derived.
The controller is designed to tracks variations of speed references and stabilizes the output speed during load
variations. The BLDC has some advantages compare to the others type of motors, however the nonlinearity of
the BLDC motor drive characteristics, because it is difficult to handle by using conventional proportionalintegral
(PI) controller. The BLDC motor is fed from the inverter where the rotor position and current
controller is the input. In order to overcome this main problem, the fuzzy logic control is learned continuously
and gradually becomes the main effective control. The effectiveness of the proposed method is verified by
develop simulation model in MATLAB-Simulink program. The simulation results show that the proposed fuzzy
logic controller (FLC) produce significant improvement control performance compare to the PI controller for
both condition controlling speed reference variations and load disturbance variations. Fuzzy logic is introduced
in order to suppressing the chattering and enhancing the robustness of the controlled system. Fuzzy boundary
layer is developed to provide smother transition to the equivalent control. Smaller overshoot in the speed
response and much better disturbance rejecting capabilities.
Comparison Analysis of Indirect FOC Induction Motor Drive using PI, Anti-Wind...IAES-IJPEDS
This paper presents the speed performance analysis of indirect Field Oriented Control (FOC) induction motor drive by applying Proportional Integral (PI) controller, PI with Anti-Windup (PIAW) and Pre- Filter (PF). The objective of this experiment is to have quantitative comparison between the controller strategies towards the performance of the motor in term of speed tracking and load rejection capability in low, medium and rated speed operation. In the first part, PI controller is applied to the FOC induction motor drive which the gain is obtained based on determined Induction Motor (IM) motor parameters. Secondly an AWPI strategy is added to the outer loop and finally, PF is added to the system. The Space Vector Pulse Width Modulation (SVPWM) technique is used to control the voltage source inverter and complete vector control scheme of the IM drive is tested by using a DSpace 1103 controller board. The analysis of the results shows that, the PI and AWPI controller schemes produce similar performance at low speed operation. However, for the medium and rated speed operation the AWPI scheme shown significant improvement in reducing the overshoot problem and improving the setting time. The PF scheme on the other hand, produces a slower speed and torque response for all tested speed operation. All schemes show similar performance for load disturbance rejection capability.
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).
Digital Implementation of Fuzzy Logic Controller for Real Time Position Contr...IOSR Journals
Fuzzy Logic Controller (FLC) systems have emerged as one of the most promising areas for
Industrial Applications. The highly growth of fuzzy logic applications led to the need of finding efficient way to
hardware implementation. Field Programmable Gate Array (FPGA) is the most important tool for hardware
implementation due to low consumption of energy, high speed of operation and large capacity of data storage.
In this paper, instead of an introduction to fuzzy logic control methodology, we have demonstrated the
implementation of a FLC through the use of the Very high speed integrated circuits Hardware Description
Language (VHDL) code. FLC is designed for position control of BLDC Motor. VHDL has been used to develop
FLC on FPGA. A Mamdani type FLC structure has been used to obtain the controller output. The controller
algorithm developed synthesized, simulated and implemented on FPGA Spartan 3E board.
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.
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 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.
PI controller design for indirect vector controlled induction motorISA Interchange
Decoupling of the stator currents is important for smoother torque response of indirect vector controlled induction motors. Typically, feedforward decoupling is used to take care of current coupling that requires exact knowledge of motor parameters, additional circuitry and signal processing. In this paper, a method is proposed to design the regulating proportional-integral gains that minimize coupling without any requirement of the additional decoupler. The variation of the coupling terms for change in load torque is considered as the performance measure. An iterative linear matrix inequality based H∞ control design approach is used to obtain the controller gains. A comparison between the feedforward and the proposed decoupling schemes is presented through simulation and experimental results. The results show that the proposed scheme is simple yet effective even without additional block or burden on signal processing.
Dynamic Simulation of Induction Motor Drive using Neuro Controlleridescitation
Induction Motors are widely used in Industries, because of the low maintenance
and robustness. Speed Control of Induction motor can be obtained by maximum torque and
efficiency. Apart from other techniques Artificial Intelligence (AI) techniques, particularly
the neural networks, improves the performance & operation of induction motor drives. This
paper presents dynamic simulation of induction motor drive using neuro controller. The
integrated environment allows users to compare simulation results between conventional,
Fuzzy and Neural Network controller (NNW).The performance of fuzzy logic and artificial
neural network based controller's are compared with that of the conventional proportional
integral controller. The dynamic Modeling and Simulation of Induction motor is done using
MATLAB/SIMULINK and the dynamic performance of induction motor drive has been
analyzed for artificial intelligent controller.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This paper presents an analysis by which the dynamic performances of a permanent magnet
brushless dc (PMBLDC) motor drive with different speed controllers can be successfully predicted. The
control structure of the proposed drive system is described. The dynamics of the drive system with a
classical proportional-integral-derivative (PID) and Fuzzy-Logic (FL) speed controllers are presented.
The simulation results for different parameters and operation modes of the drive system are investigated
and compared. The results with FL speed controller show improvement in transient response of the
PMBLDC drive over conventional PID controller. Moreover, useful conclusions stemmed from such a
study which is thought of good use and valuable for users of these controllers
Design of Fuzzy Logic Controller for Speed Regulation of BLDC motor using MATLABijsrd.com
Brushless DC (BLDC) motors drives are one of the electrical drives that are rapidly gaining popularity, due to their high efficiency, good dynamic response and low maintenance. The design and development of a BLDC motor drive for commercial applications is presented. The aim of paper is to design a simulation model of inverter fed PMBLDC motor with Fuzzy logic controller. Fuzzy logic controller is developed using fuzzy logic tool box which is available in Matlab. FIS editor used to create .FIS file which contains the Fuzzy Logic Membership function and Rule base. And membership functions of desired output. After creating .FIS file it is implemented in the Matlab Simulink. And the BLDC motor is run satisfactorily using the Fuzzy logic controller.
Power optimisation scheme of induction motor using FLC for electric vehicleAsoka Technologies
In electric vehicles (EVs) and hybrid EVs, energy efficiency is essential where the energy storage is limited. Adding to its high stability and low cost, the induction motor efficiency improves with loss minimisation. Also, it can consume more power than the actual need to perform its working when it is operating in less than full load condition. This study proposes a control strategy based on the fuzzy logic control (FLC) for EV applications. FLC controller can improve the starting current amplitude and saves more power. Through the MATLAB/SIMULINK software package, the performance of this control was verified through simulation. As compared with the conventional proportional integral derivative controller, the simulation schemes show good, high-performance results in time-domain response and rapid rejection of system-affected disturbance. Therefore, the core losses of the induction motor are greatly reduced, and in this way improves the efficiency of the driving system. Finally, the suggested control system is validated by the experimental results obtained in the authors’ laboratory, which are in good agreement with the simulation results.
Speed Control of Induction Motor by Using Intelligence TechniquesIJERA Editor
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.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
Speed Control of Brushless Dc Motor Using Fuzzy Logic Controlleriosrjce
This paper presents a control scheme of a fuzzy logic for the brushless direct current (BLDC)
permanent magnet motor drives. The mathematical model of BLDC motor and fuzzy logic algorithm is derived.
The controller is designed to tracks variations of speed references and stabilizes the output speed during load
variations. The BLDC has some advantages compare to the others type of motors, however the nonlinearity of
the BLDC motor drive characteristics, because it is difficult to handle by using conventional proportionalintegral
(PI) controller. The BLDC motor is fed from the inverter where the rotor position and current
controller is the input. In order to overcome this main problem, the fuzzy logic control is learned continuously
and gradually becomes the main effective control. The effectiveness of the proposed method is verified by
develop simulation model in MATLAB-Simulink program. The simulation results show that the proposed fuzzy
logic controller (FLC) produce significant improvement control performance compare to the PI controller for
both condition controlling speed reference variations and load disturbance variations. Fuzzy logic is introduced
in order to suppressing the chattering and enhancing the robustness of the controlled system. Fuzzy boundary
layer is developed to provide smother transition to the equivalent control. Smaller overshoot in the speed
response and much better disturbance rejecting capabilities.
Comparison Analysis of Indirect FOC Induction Motor Drive using PI, Anti-Wind...IAES-IJPEDS
This paper presents the speed performance analysis of indirect Field Oriented Control (FOC) induction motor drive by applying Proportional Integral (PI) controller, PI with Anti-Windup (PIAW) and Pre- Filter (PF). The objective of this experiment is to have quantitative comparison between the controller strategies towards the performance of the motor in term of speed tracking and load rejection capability in low, medium and rated speed operation. In the first part, PI controller is applied to the FOC induction motor drive which the gain is obtained based on determined Induction Motor (IM) motor parameters. Secondly an AWPI strategy is added to the outer loop and finally, PF is added to the system. The Space Vector Pulse Width Modulation (SVPWM) technique is used to control the voltage source inverter and complete vector control scheme of the IM drive is tested by using a DSpace 1103 controller board. The analysis of the results shows that, the PI and AWPI controller schemes produce similar performance at low speed operation. However, for the medium and rated speed operation the AWPI scheme shown significant improvement in reducing the overshoot problem and improving the setting time. The PF scheme on the other hand, produces a slower speed and torque response for all tested speed operation. All schemes show similar performance for load disturbance rejection capability.
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).
Digital Implementation of Fuzzy Logic Controller for Real Time Position Contr...IOSR Journals
Fuzzy Logic Controller (FLC) systems have emerged as one of the most promising areas for
Industrial Applications. The highly growth of fuzzy logic applications led to the need of finding efficient way to
hardware implementation. Field Programmable Gate Array (FPGA) is the most important tool for hardware
implementation due to low consumption of energy, high speed of operation and large capacity of data storage.
In this paper, instead of an introduction to fuzzy logic control methodology, we have demonstrated the
implementation of a FLC through the use of the Very high speed integrated circuits Hardware Description
Language (VHDL) code. FLC is designed for position control of BLDC Motor. VHDL has been used to develop
FLC on FPGA. A Mamdani type FLC structure has been used to obtain the controller output. The controller
algorithm developed synthesized, simulated and implemented on FPGA Spartan 3E board.
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is an open access international journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IOSR Journal of Applied Physics (IOSR-JAP) is an open access international journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
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.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The aim of this paper is to prove that fuzzy logic algorithm is a suitable control technique for fast processes such as electrical machines. This theory has been experimented on different kinds of electrical machines such as stepping motors, dc motors and induction machines (with 6 phases) and the experimental results show that the proposed fuzzy logic algorithm is the most suitable control technique for electrical machines since this algorithm is not time consuming and it is also robust between plant parameters variations.
Implementation of pi, fuzzy & ann controllers to improve dynamic response...eSAT Journals
Abstract Nowadays, vector controlled induction motor drives with variable speed applications are widely used in order to achieve good dynamic performance and wide speed control. In this paper a new method of controlling technique based on Artificial Neural Network is proposed to improve the speed control of indirect vector controlled induction motor drive. Indirect vector controlled induction motor with conventional PI controller is developed and is replaced with Fuzzy logic controller to overcome the problem of overshoot occurred in conventional PI controller. To obtain quick steady state response and better speed control, ANN technique is proposed and implemented using MATLAB/Simulink. In this paper the speed, torque and stator voltage responses with conventional PI controller, Fuzzy logic controller and proposed artificial neural network based controller are compared and found that the proposed ANN based controller showed increased dynamic performance. Keywords: ANN, FLC, PI controller, IVCIM
This work presents a fast response and stable computer based a brushed DC motor speed controller. The controller configured of gate drive circuits for H-Bridge accompanied with data acquisition unit DAQ-6211. These gate drive circuits include, phase comparator, current booster and wave forms cleaning circuits. An optical encoder is used for motor speed to frequency conversion. The CD4046 PLL chip compares phases of the encoder output frequency (motor speed) with a reference frequency (desired speed). The obtained phase difference (error) is used to allocate the suitable PWM duty cycles. An H-Bridge BJT switches driven by PWM is interfaced with the motor. The system hardware is provided with a simple and accurate data acquisition unit DAQ-6211 to be interfaced with the LabVIEW software Package. This allows monitoring and storing the different measured data of this platform. The system relative stability is determined and examined based on the Bode plot analysis and design. Then the relative stability criterion (Phase Margin) is measured the closed-loop stability of the system. This system considers the fast feedback response with indication of its stability state as well as the stable wide dynamic range. It compensates the changes in system parameters due to the environmental effects and other disturbances.
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 –
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F010214352
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 10, Issue 2 Ver. I (Mar – Apr. 2015), PP 43-52
www.iosrjournals.org
DOI: 10.9790/1676-10214352 www.iosrjournals.org 43 | Page
Comparative Study of Speed Control of Induction Motor Using PI
and Fuzzy Logic Controller
Anmol Aggarwal, J. N. Rai, Maulik Kandpal
Department of Electrical Engineering, Delhi Technological University
(Formerly Delhi College of Engineering)
Abstract:This paper proposes the idea of using “Fuzzy Logic Technique” in estimating motor speed and
controlling it for Induction motor. The Induction motor is modeled using dq axis theory. The main objective of
this project is to develop a fuzzy logic based controller to control the speed of the induction motor, employing
the scalar control model. The voltage and frequency input to the induction motor are to be controlled in order to
obtain the desired speed response. The designed Fuzzy Logic Controllers performance is also weighed against
with that of a PI controller. For V/f speed control of the induction motor, a reference speed has been set and the
control architecture includes a rule base of 49 rules. These rules portray a nonchalant relationship between two
inputs i.e. speed error (e), change in speed error (∆e) and an output i.e change ofcontrol (ωsl)
Keywords:InductionMotor,V/f induction motor speed control, dq axis theory, Fuzzy Logic controller,
MamdaniArchitecture, Membership functions and PI controller
I. Introduction
The induction motor is an important class of electric machines which finds wide applicability as a
motor in industry and in its single phase form in several domestic applications. More than 85% of industrial
motor used today are in fact induction motors. It is a singly fed motor (stator-fed). [1].
The speed of the induction motor is given by:
n= (1-s) ns (1)
Where,
n=Rotor speed
s=Slip
ns =Synchronous speed
The synchronous speed in terms of frequency is expressed as:
ns=(120*f)/p (2)
Where,
f=Supply frequency
n=Number of poles
From equation (2) it’s evident that the synchronous speed and hence the rotor speed using equation (1)
can be controlled by varying supply frequency. Voltage induced in stator is given by E1 = Kɸf, where K is a
constant,ɸ is the air-gap flux and f is the supply frequency. Neglecting the stator voltage drop (which is hardly
10% of the supply voltage), terminal voltage V1= Kɸf .It is evident that a reduction in the supply frequency
without a change in the terminal voltage causes an increase in the air-gap flux (hence shifting the operating point
of the motor towards saturation).Hence we keep V/f ratio constant so as to avoid any discrepancy in motor
operation. This method is also known as Scalar Control.[8][9]
II. Induction Motor-A General Overview
The stator of an induction motor is wound for three phases. Fig 1 shows the stator of an induction motor.[1]
Fig1: Stator of an Induction Motor
2. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 44 | Page
Two types of constructions are employed for the rotor i.e. Squirrel - Cage Rotor andWoundRotor. The
rotor core is of laminated construction with slots suitably punched in for accommodating the rotor winding
/rotor bars. Fig 2 & Fig 3 shows squirrel cage and wound rotor respectively.[1]
Fig2:Squirrel Cage rotor Fig3:Wound Rotor
Speed Control Techniques in Induction Motor
There are two basic ways of speed control, namely
(i) Slip-control for fixed synchronous speed.
(ii) Control of synchronous speed.
A stricter sorting reveals the following methods:
(i) Pole changing.
(ii) Stator voltage control.
(iii) Supply frequency control.
(iv)Eddy-current coupling.
(v) Rotor resistance control.
(vi )Slip power recovery [1][8]
III. PI Controller
The PI controller computes the controlled output by calculating the proportional and integral errors and
summing these two components to compute the output.
Fig 4: PI Controller block diagram.
In PI controller due to presence of the integral term, steady state error of speed is zero, making the
system quite accurate. It does not require high gain as required in proportional gain controller. However it has
certain drawbacks like if very fast response is desired, the penalty paid is a higher overshoot which is
undesirable. The PID controller offers a very efficient solution to numerous control problems in the real world.
[2][4][5].In our MATLAB model (Fig 14) we have used the inbuilt PI controller present in Simulink library of
MATLAB.
IV. Fuzzy Logic Controller
Fuzzy logic control is a control algorithm based on a linguistic control strategy, which is derived from
expert knowledge into an automatic control strategy. While the other control systems use difficult mathematical
calculation to provide a model of the controlled plant, it only uses simple mathematical calculation to simulate
the expert knowledge. Although it doesn't need any difficult mathematical calculation however it gives good
3. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 45 | Page
performance in a control system. Thus, it can be one of the best available answers today for a broad class of
challenging control problems. [2][3][4]
Fig 5:Block diagram of a Fuzzy Logic Controller
The fuzzifier scales and maps input variables to fuzzy sets.Inference engine with the help of rule base
does the approximate reasoning and deduce the control action.Defuzzifier converts fuzzy output values to
control actions.[7]
Advantage Of Using Fuzzy Technique
Fuzzy technique have gained in wide acceptance in expert systems, control units and in wide range of
applications because of fast adaptation, high degree of tolerance, smooth operation, reduction in the effect of
non-linearity, easy if-else logics and inherent approximation adaptability. [6]
A fuzzy logic controller (FLC) has already been proved analytically to be equivalent to a non-linear PI
controller when a non-linear defuzzification method is used. Also, the result from the comparisons of
conventional and fuzzy logic control techniques in the form of a FLC and fuzzy compensator showed fuzzy
logic can reduce the effects of non-linearity in a DC motor and improve the performance of a controller.
[2][3][4]
V. Rule Base For Speed Control Of Induction Motor
The design of a Fuzzy Logic Controller requires the choice of Membership Functions. The membership
functions should be chosen such that they cover the whole universe of discourse. It should be taken care that the
membership functions overlap each other. This is done in order to avoid any kind of discontinuity with respect
to the minor changes in the inputs. To achieve finer control, the membership functions near the zero region
should be made narrow. Wider membership functions away from the zero region provides faster response to the
system. Hence, the membership functions should be adjusted accordingly. After the appropriate membership
functions are chosen, a rule base should be created. It consists of a number of Fuzzy If-Then rules that
completely define the behaviour of the system. These rules very much resemble the human thought process,
thereby providing artificial intelligence to the system.[10]In this paper, the speed controller make use of 49
rules mentioned in the matrix below, based on which Fuzzy Logic controller operates to give the desired result.
Fig 6:Rule Matrix for control output, change of control(ωsl)
4. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 46 | Page
Where,
e Speed error NS Negative small
∆e Change in speed error Z Zero
ωsl Change of control PL Positive large
NL Negative large PM Positive medium
NM Negative medium PS Positive small
NLM Negative large medium PLM Positive large medium
NMS Negative medium small PMS Positive medium small
The general considerations in the design of the controller are:
1. If both error and change in speed error are zero maintain the present control setting i.e. output=0.
2. If the error is not zero but is approaching this value at a satisfactory rate, then maintain the present
controlsetting.
3. If the error is growing then change the control signal output depending on the magnitude and sign of error
and change in speed error to force the error towards zero.
The typical rules of the table are read as(shaded portion in matrix):
IF e=Z AND ∆e = NS THEN ωsl = NS
IF e=PS AND ∆e = NS THEN ωsl = ZE
IF e=ZEAND ∆e= ZETHEN ωsl = ZE
IF e=PS AND ∆e= ZETHEN ωsl= PS
Fig 7:FIS Editor
5. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 47 | Page
Fig 8:Membership Function for change in speed
Fig 9:Membership Function for change in speed error
Fig 10:Membership function for output
6. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 48 | Page
Fig 11:Rules
Fig 12:Rule Viewer
Fig13:Surface Viewer
7. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 49 | Page
Fig14:Matlab/Simulink Model of PI Controller
As shown in the simulink diagram, “wm*” is chosen as the reference speed. The use of speed as
reference signal is justified as the output of the system is speed and our aim is to control the speed of the
induction motor. A tacho-generator, attached to the shaft of the induction motor, provides the current speed of
the motor, “wm” which is compared with the reference speed “wm*”, thus providing us with the speed error (e).
This mechanism is called the feedback mechanism. The information about the instantaneous state of the output
is fed back to the input which in turn is used to revise the same in order to achieve a desired output. [1]Speed
error (e) is fed to the PI controller, to give an output variable. This output variable is then added to the motor
speed “wm” which in turn forms the input to the V/f controller.
Electrical System Equations
vs=Rsis+ dλs/dt+ωkMλs (3)
vr=Rrir+ dλr/dt+(ωk-ωm)Mλr (4)
Where the space vector f= [fdfq]T
and the π/2 rotational operator M=
−1 0
0 1
Flux Linkage-Current Relations
λs = Lsis+Lmir (5)
is= Γsλs-Γmλr (6)
λr = Lm is+Lrir (7)
ir = -Γmλs+Γrλr (8)
Ls = Lm +Lsl (9)
Lr = Lm + Lrl (10)
Γs =Lr/∆ (11)
Γr =Ls/∆ (12)
Γrm = Lm/∆ (13)
∆ = LmLsl + LmLrl +LslLrl (14)
Mechanical System Equations
Te = J(dωmech/dt)+Bmωmech+Tl (15)
Where,
Te = k(λs× is ) = k (Mλs•is) = k(λdsiqs – λqsids) or, (16)
Te = k(ir× λr ) = kLm (ir×is) = k(Lm/Lr)(λr× is) = kΓm(λr×λs) and, (17)
ωmech = (2/p) ωm, k=(3/2)(p/2)
Where,
8. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 50 | Page
v Voltage space vector (V) Γ Inverse inductance (H-1
) × Cross product
i Current space vector (A) f0 Base frequency (Hz) • Dot product
λ Flux linkage space vector (Wb) ω0 Base frequency (rad/s) M Rotation
R Resistance (Ω) ωk Speed of dq frame (rad/s) s Stator
L Inductance (H) ωm Rotor speed (rad/s) r Rotor
Te Electromagnetic torque (Nm) J Moment of inertia (kg.m2
) d Direct axis
TL Load torque (Nm) P Number of poles q Quadrature axis
Fig15: Space vector model of Induction Motor
Fig16: Model for flux current relations
The space vector model of induction motor and the model for flux current relations has been modelled using
equations (5) to (17).
9. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 51 | Page
Time in seconds
Fig 17: Speed V/S Time Response of PI controlled Induction Motor
Fig18:Matlab/Simulink Model of Fuzzy Controller
The model for Fuzzy Controller works on the same principle as PI controller explained above except that the
change-both e and de (Fig 18) are fed to the fuzzifier for fuzzification. The inference system then processes
these two fuzzy inputs using the fuzzy control rules and the database, which are defined by the programmer
based on the chosen membership function and fuzzy rule table, to give an output fuzzy variable. The fuzzy
output thus obtained is defuzzified by the defuzzifier to give a crisp value.[1]
Motor speed (pu)
10. Comparative Study of Speed Control of Induction Motor Using PI and Fuzzy Logic Controller
DOI: 10.9790/1676-10214352 www.iosrjournals.org 52 | Page
Time in seconds
Fig 19:Speed V/S Time Response using Fuzzy logic controller
VI. Conclusion
The background of Induction Motor is studied and a study of the characteristics of induction motor is
done. Graph for the speed response of induction motor using fuzzy logic controller (FLC) is successfully
simulated in MATLAB and compared with graph for the speed response of Induction Motor with PI controller.It
is established that fuzzy logic controller has finer performance in comparison to PI controller as steady state
error (SSE) is zero in FLC whereas in PI controller the SSE is about 2%. Even the rise time in FLC is less as
compared with PI controller which shows FLC have faster dynamic response as compared to designed PI
controller.
Appendix-A
Specifications Of The Induction Motor
Stator Resistance = 0.050pu
Rotor Resistance = 0.030pu
Stator Leakage Inductance = 0.20 pu
Rotor Leakage Inductance = 0.02pu
Magnetizing Inductance = 6.0 pu
Base Frequency = 2*π*50 rad/s
Number of Poles = 2
Moment of Inertia = 1.2pu
Viscous Friction Coefficient = 2*10-5
pu
References
[1]. Electric machinery, Fitzgerald & Kingsley
[2]. Modern Control Engineering, Katsuhiko Ogata
[3]. Zimmermann, H. (2001). Fuzzy Set Theory and Its Applications. Boston: Kluwer Academic Publishers. ISBN 0-7923-7435-5
[4]. J. G. Ziegler and N. B. Nichols, “Optimum settings for automatic controllers,” Trans. ASME, vol. 64, pp. 759–768, 1942.
[5]. G. Mallesham and A. Rajani, “Automatic Tuning of PID Controller using Fuzzy Logic,” 8th International Conference on
Development and Application Systems, Suceava, Romania, pp. 120 – 126, 2006.
[6]. PavolFedorand DanielaPerduková, “A Simple Fuzzy Controller Structure,” ActaElectrotechnica et Informatica No. 4, Vol. 5, pp. 1-
4, 2005.
[7]. J.-S. R. Jang, C.-T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing,” Pearson Education Pte. Ltd., ISBN 81-297-0324-6, 1997,
chap. 2, chap. 3, chap. 4.
[8]. Gopal K. Dubey, “Fundamentals of Electrical Drives”, Narosa Publishing House Pvt. Ltd., 2001, chap. 6.
[9]. Yau-Tze Kao and Chang-Huan Liu, “Analysis and Design of Microprocessor-Based Vector-Controlled Induction Motor Drives,”
IEEE Transactions on Industrial Electronics, Vol. 39, pp. 46 – 54, 1 February, 1992.
[10]. M.Chow, A. Menozzi and F. Holcomb ,”On the Comparison of Emerging and Conventional Techniques for DC Motor Control,”
proc. IECON , pp. 1008-1013, 1992.
Motor speed (pu)