This document summarizes a research paper that compares PI and neuro-fuzzy controllers for direct torque control of induction motor drives. It first provides background on direct torque control and issues with PI controllers, such as complex tuning and overshoot problems. It then introduces neuro-fuzzy control as an alternative approach to address these issues. The document outlines the proposed neuro-fuzzy controller structure and simulation results comparing its performance to a PI controller under various operating conditions. The results showed that the neuro-fuzzy controller reduced overshoot and improved performance relative to the PI controller.
In this paper, DTC is applied for two-level inverter fed IM drives based on neuronal hysteresis comparators and The Direct Torque Control (DTC) is known to produce quick and robust response in AC drive system. However, during steady state, torque, flux and current ripple. An improvement of electric drive system can be obtained using a DTC method based on ANNs which reduces the torque and flux ripples, the estimated the rotor speed using the KUBOTA observer method based on measurements of electrical quantities of the motor. The validity of the proposed methods is confirmed by the simulation results.The THD (Total Harmonic Distortion) of stator current, torque ripple and stator flux ripple are determined and compared with conventional DTC control scheme using Matlab/Simulink environment.
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.
Design and Implementation of Speed Control of Induction Motor using Arduino B...ijtsrd
The low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control. Soft computing technique – the Fuzzy logic is applied in this work .We have carried on with the hardware implementation for speed control of induction motor using the fuzzy logic control . Using the arduino micro controller, we have developed a hardware setup which is able to control the speed of induction motor by using Arduino Uno .We can conclude that we have been able to get a good control over the speed of motor. Talat Jabeen | Ganesh Wakte ""Design and Implementation of Speed Control of Induction Motor using Arduino Based FLC"" 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/ijtsrd23684.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23684/design-and-implementation-of-speed-control-of-induction-motor-using-arduino-based-flc/talat-jabeen
Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with ...IJECEIAES
This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.
In this paper, DTC is applied for two-level inverter fed IM drives based on neuronal hysteresis comparators and The Direct Torque Control (DTC) is known to produce quick and robust response in AC drive system. However, during steady state, torque, flux and current ripple. An improvement of electric drive system can be obtained using a DTC method based on ANNs which reduces the torque and flux ripples, the estimated the rotor speed using the KUBOTA observer method based on measurements of electrical quantities of the motor. The validity of the proposed methods is confirmed by the simulation results.The THD (Total Harmonic Distortion) of stator current, torque ripple and stator flux ripple are determined and compared with conventional DTC control scheme using Matlab/Simulink environment.
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.
Design and Implementation of Speed Control of Induction Motor using Arduino B...ijtsrd
The low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control. Soft computing technique – the Fuzzy logic is applied in this work .We have carried on with the hardware implementation for speed control of induction motor using the fuzzy logic control . Using the arduino micro controller, we have developed a hardware setup which is able to control the speed of induction motor by using Arduino Uno .We can conclude that we have been able to get a good control over the speed of motor. Talat Jabeen | Ganesh Wakte ""Design and Implementation of Speed Control of Induction Motor using Arduino Based FLC"" 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/ijtsrd23684.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23684/design-and-implementation-of-speed-control-of-induction-motor-using-arduino-based-flc/talat-jabeen
Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with ...IJECEIAES
This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.
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.
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.
Controlling power flow losses in upfc system using adaptive neuro fuzzy contr...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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
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.
Neural network based vector control of induction motorcsandit
Stator current drift compensation of induction motor based on RBF neural network is proposed
here. In vector control of induction motor decoupling of speed and rotor flux equations and
their simultaneous control are used to achieve the highest efficiency and fast dynamic
performance. The highest efficiency is reached when the proper flux is selected and as a result
of dynamic decoupling of speed and rotor flux equations, the rotor flux can be modified to
achieve the highest efficiency and make the speed be at its desired value. The precise control of
these changes can also be done using radial basis function neural network (RBFNN). Once
neural network gets trained then it is able to differentiate between normal and fault conditions
and therefore acts in accordance to the change that could bring back the system to normal
condition. Here, neural network is used to compute the appropriate set of voltage and frequency
to achieve the maximum efficiency for any value of operating torque and motor speed.
NEURAL NETWORK BASED VECTOR CONTROL OF INDUCTION MOTORcsandit
Stator current drift compensation of induction motor based on RBF neural network is proposed here. In vector control of induction motor decoupling of speed and rotor flux equations and their simultaneous control are used to achieve the highest efficiency and fast dynamic
performance. The highest efficiency is reached when the proper flux is selected and as a result of dynamic decoupling of speed and rotor flux equations, the rotor flux can be modified to achieve the highest efficiency and make the speed be at its desired value. The precise control of these changes can also be done using radial basis function neural network (RBFNN). Once
neural network gets trained then it is able to differentiate between normal and fault conditions and therefore acts in accordance to the change that could bring back the system to normal condition. Here, neural network is used to compute the appropriate set of voltage and frequency
to achieve the maximum efficiency for any value of operating torque and motor speed.
Arm based automatic control system of nano positioning stage for micromanufac...csandit
A microcontroller based control system to drive the Physik Instrumente (PI )
piezoelectric ultrasonic nano-positioning (PUN) stage for a micro-factory has been
proposed by the author. The tuning parameters of the PI Line Controller are chosen
such that the PUN stage shows optimum step response. The microcontroller i.e.
LPC2478R provides the user with the choices of operations on the 3.2” QVGA LCD
screen and the choice can be made by a 5-key joystick. The PUN stage moves in
different geometrical patterns as chosen by the user. The stage is placed in the
workspace of the Clark-MXRR,Inc. CPA-2101 femto-second laser. Different patterns
are made on the material in question. As compared to the previous works in this area,
the user is given the power for position control, real time tracking, and trajectory
planning of the actuator. The user interface has been made very easy to comprehend.
The repeatability of tasks, portability of the as- sembly, the reduction in the size of the
system , power consumption and the human involvement are the major achievements
after the inclusion of a microcontroller.
Implementation of Transformer Protection by Intelligent Electronic Device for...IJERA Editor
Protection of power system equipments was traditionally done by using electromagnetic relay, static relays, and
numerical relays. At present the microprocessor based relays are replacing the old Electromagnetic relays
because of their high level accuracy and fast operation. RET670(Transformer protection relay ), an IED
(INTELLIGENT ELECTRONIC DEVICE) provides fast and selective protection, monitoring, and control of all
types of transformer. The configured IED is tested under different fault conditions simulated by using mobile test
kit to ensure IED’s reliable operation on site. With preconfigured algorithms, the IED will automatically
reconfigure the network in case of a fault, and a service restoration is carried out within milliseconds by giving
trip signal to the corresponding Circuit breakers. On receiving the trip signal the circuit breaker operates
providing quicker isolation of transformers under the fault condition. This enables to have a complete and an
adequate protection to the specified power transformer.
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.
Stabilization Of Power System Using Artificial Intelligence Based SystemIJARIIT
This paper reviews limitations of traditional control system and modern control system controllers, which are
overcome to some extent using artificial intelligent techniques, such as ANN, Fuzzy Logic, Expert System, Particle Swarm
Optimization, Genetic Algorithm, etc. The review shows that efforts are made towards Power System Stabilizer based on
Artificial Intelligent Techniques, which will give positive impact on the system stabilities and improve system performances.
Autotuning of pid controller for robot arm and magnet levitation planteSAT Journals
Abstract
One of the most essential work of the control engineer is tuning of controller. Majority of the controller used in industry are of the
PID type. An auto tuning is one of the method of controller tuning in which tuning of the parameters of controller is done
automatically and possibly, without any user interaction expect from initiating the operation. Present study emphasis on the relay
based auto tuning of PID controller. An auto-tuning method is implemented based on a relay experiment to determine the ultimate
gain and the ultimate period, with which the PID parameters are obtained using the Ziegler-Nichols tuning rules. An auto tuning
of robot arm model and magnet levitation model are carried out. Performance of relay based auto tuning on the basis of integral
square error is better than artificial neural network.
Keywords: Relay auto tuning, PID, FOPDT, SOPDT, Integral square error.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
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.
Controlling power flow losses in upfc system using adaptive neuro fuzzy contr...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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
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.
Neural network based vector control of induction motorcsandit
Stator current drift compensation of induction motor based on RBF neural network is proposed
here. In vector control of induction motor decoupling of speed and rotor flux equations and
their simultaneous control are used to achieve the highest efficiency and fast dynamic
performance. The highest efficiency is reached when the proper flux is selected and as a result
of dynamic decoupling of speed and rotor flux equations, the rotor flux can be modified to
achieve the highest efficiency and make the speed be at its desired value. The precise control of
these changes can also be done using radial basis function neural network (RBFNN). Once
neural network gets trained then it is able to differentiate between normal and fault conditions
and therefore acts in accordance to the change that could bring back the system to normal
condition. Here, neural network is used to compute the appropriate set of voltage and frequency
to achieve the maximum efficiency for any value of operating torque and motor speed.
NEURAL NETWORK BASED VECTOR CONTROL OF INDUCTION MOTORcsandit
Stator current drift compensation of induction motor based on RBF neural network is proposed here. In vector control of induction motor decoupling of speed and rotor flux equations and their simultaneous control are used to achieve the highest efficiency and fast dynamic
performance. The highest efficiency is reached when the proper flux is selected and as a result of dynamic decoupling of speed and rotor flux equations, the rotor flux can be modified to achieve the highest efficiency and make the speed be at its desired value. The precise control of these changes can also be done using radial basis function neural network (RBFNN). Once
neural network gets trained then it is able to differentiate between normal and fault conditions and therefore acts in accordance to the change that could bring back the system to normal condition. Here, neural network is used to compute the appropriate set of voltage and frequency
to achieve the maximum efficiency for any value of operating torque and motor speed.
Arm based automatic control system of nano positioning stage for micromanufac...csandit
A microcontroller based control system to drive the Physik Instrumente (PI )
piezoelectric ultrasonic nano-positioning (PUN) stage for a micro-factory has been
proposed by the author. The tuning parameters of the PI Line Controller are chosen
such that the PUN stage shows optimum step response. The microcontroller i.e.
LPC2478R provides the user with the choices of operations on the 3.2” QVGA LCD
screen and the choice can be made by a 5-key joystick. The PUN stage moves in
different geometrical patterns as chosen by the user. The stage is placed in the
workspace of the Clark-MXRR,Inc. CPA-2101 femto-second laser. Different patterns
are made on the material in question. As compared to the previous works in this area,
the user is given the power for position control, real time tracking, and trajectory
planning of the actuator. The user interface has been made very easy to comprehend.
The repeatability of tasks, portability of the as- sembly, the reduction in the size of the
system , power consumption and the human involvement are the major achievements
after the inclusion of a microcontroller.
Implementation of Transformer Protection by Intelligent Electronic Device for...IJERA Editor
Protection of power system equipments was traditionally done by using electromagnetic relay, static relays, and
numerical relays. At present the microprocessor based relays are replacing the old Electromagnetic relays
because of their high level accuracy and fast operation. RET670(Transformer protection relay ), an IED
(INTELLIGENT ELECTRONIC DEVICE) provides fast and selective protection, monitoring, and control of all
types of transformer. The configured IED is tested under different fault conditions simulated by using mobile test
kit to ensure IED’s reliable operation on site. With preconfigured algorithms, the IED will automatically
reconfigure the network in case of a fault, and a service restoration is carried out within milliseconds by giving
trip signal to the corresponding Circuit breakers. On receiving the trip signal the circuit breaker operates
providing quicker isolation of transformers under the fault condition. This enables to have a complete and an
adequate protection to the specified power transformer.
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.
Stabilization Of Power System Using Artificial Intelligence Based SystemIJARIIT
This paper reviews limitations of traditional control system and modern control system controllers, which are
overcome to some extent using artificial intelligent techniques, such as ANN, Fuzzy Logic, Expert System, Particle Swarm
Optimization, Genetic Algorithm, etc. The review shows that efforts are made towards Power System Stabilizer based on
Artificial Intelligent Techniques, which will give positive impact on the system stabilities and improve system performances.
Autotuning of pid controller for robot arm and magnet levitation planteSAT Journals
Abstract
One of the most essential work of the control engineer is tuning of controller. Majority of the controller used in industry are of the
PID type. An auto tuning is one of the method of controller tuning in which tuning of the parameters of controller is done
automatically and possibly, without any user interaction expect from initiating the operation. Present study emphasis on the relay
based auto tuning of PID controller. An auto-tuning method is implemented based on a relay experiment to determine the ultimate
gain and the ultimate period, with which the PID parameters are obtained using the Ziegler-Nichols tuning rules. An auto tuning
of robot arm model and magnet levitation model are carried out. Performance of relay based auto tuning on the basis of integral
square error is better than artificial neural network.
Keywords: Relay auto tuning, PID, FOPDT, SOPDT, Integral square error.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Financiamiento de proyectos transnacionales. El caso E-LISRIBDA 2009
Fernanda Peset
Universidad Politécnica de Valencia (España)
Imma Subirats
FAO (Italia)
Antonia Ferrer
Universidad Politécnica de Valencia (España)
Rafael Aleixandre
CSIC-Universitat de València (España)
Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. DTC has some drawbacks, such as the torque and flux ripple. The control scheme performance relies on the accurate selection of the switching voltage vector. This proposed simple structured neural network based new identification method for flux position estimation, sector selection and stator voltage vector selection for induction motors using direct torque control (DTC) method. The ANN based speed controller has been introduced to achieve good dynamic performance of induction motor drive. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. Proposed simple structured network facilitates a short training and processing times. The stator flux is estimated by using the modified integration with amplitude limiter algorithms to overcome drawbacks of pure integrator. The conventional flux position estimator, sector selector and stator voltage vector selector based modified direct torque control (MDTC) scheme compared with the proposed scheme and the results are validated through both by simulation and experimentation.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Direct torque control using neural network approacheSAT Journals
Abstract Direct Torque Control (DTC) is one of the latest technique to control the speed of motor, in this paper, the control technique of DTC is based on when load changes then inverter switch position are changed and supply to the motor is changed, in this paper Proportional Integral (PI), Neural Network (NN) controller and Adaptive motor model is designed this is the heart of the DTC, as we know that DTC doesn’t require any feedback and sensors to measure. The NN structure is to be implemented by input output (nonlinear) mapping models and is constructed with input, output and hidden layers of sigmoid activation functions. It has been introduced as a possible solution to the real multivariate interpolation problem. To improve the performance of DTC with the modern technique using NN approach is implemented, and performance of DTC with PI controller and NN controller is done, hence, the NN approach shows the better performance than conventional PI controller. Keywords: DTC, PI, NN, Adaptive Motor Model and MATLAB.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
As the technology is fast changing, there is more and more use of machine intelligence in modern motor controllers. These controllers are employed in advanced electric motor drives in particular, the present day Induction motor drives. These systems emulate the human logic. This is particularly useful when the application has poorly defined mathematical model. In this present paper the analysis of fuzzy logic as the artificial intelligence is used. The comparative study of Fuzzy PI, Fuzzy MRAC is made. There is always a compromise of the cost and complexity. So this paper presents a new approach and its dynamic response in comparison to the Fuzzy PI and Fuzzy MRAC. The proposed controller is Fuzzy PI with scaling factors. This approach is validated with the Speed, torque responses of Indirect vector controlled Induction motor (IVCIM) drive.
Performance analysis of a single phase ac voltage controller under induction ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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).
2.a neuro fuzzy based svpwm technique for pmsm (2)EditorJST
In the present scenario, static frequency converter based variable speed synchronous motors has
become very familiar and advantage to other drive system, especially low speed and high power applications.
Unlike the induction motor, the synchronous motor can be operated at variable power factor (leading, lagging
or unity) as desired. So, there is an increasing use of synchronous motors as adjustable speed drives. The PWM
technique is very useful to VSI drive for achieving efficient and smooth operation and free from torque
pulsations and cogging, lower volume and weight and provides a higher frequency range compared to CSI
drives. Even for voltage source inverter, the commutation circuit is not needed, if the self-extinguishing
switching devices are used. This paper proposes a concept of Neuro-fuzzy based control strategy which is used
for controlling the PMSM. The total work mainly concentrates on optimum control of PMSM with maximum
voltage utilization with less switching losses.
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.
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.
The aim of this article is propose a method to improve the direct torque control and design a Fuzzy Logic based Controller which can take necessary control action to provide the desired torque and flux of an asynchronous machine. It’s widely used in the industrial application areas due to several features such as fast torque response and less dependence on the rotor parameters. The major problem that is usually associated with DTC control is the high torque ripple as it is not directly controlled. The high torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. The fuzzy logic controller is applied to reduce electromagnetic torque ripple. In this proposed technique, the two hysteresis controllers are replaced by fuzzy logic controllers and a methodology for implementation of a rule based fuzzy logic controller are presented. The simulation by Matlab/Simulink was built which includes induction motor d-q model, inverter model, fuzzy logic switching table and the stator flux and torque estimator. The validity of the proposed method is confirmed by the simulative results of the whole drive system and results are compared with conventional DTC method.
1. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
A Comparative Analysis Of PI And Neuro Fuzzy
Controllers In Direct Torque Control Of Induction Motor
Drives
A.Sudhakar*,M.Vijaya Kumar**
*(Department of Electrical and Electronics Engineering,S.V.I.T Engg College, Anantapur,India)
**(Department of Electrical Engineering,J.N.T.U,Anantapur,India.)
Abstract:
The implementation of conventional harmonic currents.
DTC in Induction Motor drives consisting of Various methods have been proposed to
PI torque controller suffers from complex overcome these drawbacks, such as variable
tuning and Overshoot problems. One of the structure control approach,fuzzy logic
various methods to tackle this problem is control,neural netwok control,adaptive control
implementation of intelligent controllers like methods have been proposed for motion control
neuro fuzzy-based controller This paper of Induction motor drive[2-12]. The PI control
presents the simulation and analysis of a is simple and offers a wide stability margin, but
Neuro fuzzy-based torque controller for DTC it incorporates tuning,overshoot problems. The
of Induction motor drives. This control fuzzy logic can compensate the system
scheme uses the speed error calculated from nonlinearities through human expertise. Yet, it
reference speed and estimated speed which relies too much on the intuition and experience
generates the estimated Torque and of the designer. The neural network can handle
compared with the actual Torque and the complicated nonlinear characteristics of the
generates the inverter switching states. In this system, but suffer from the problem of lengthy
paper a modified ANFIS structure is training and convergence time. The adaptive
proposed. This structure generates the desired control can self adjust the controller parameters
reference voltage which regulates the to adapt system parameter variations.
performance of induction motor. Unfortunately, it generally requires a reference
Comparisons and analysis under various model of the system.
operating conditions between hysteresis-based
PI torque controller and Neuro fuzzy-based The controller proposed in this paper is
torque controller are presented. The results neuro fuzzy-based controller which gives better
show that the proposed controller managed to performance compared to PI controller. The
reduce the overshoot and give better neural network is well known for its learning
performance. ability and approximation to any arbitrary
continuous function.[9] It has been proposed in
Keywords-direct torque control, induction the literature that neural networks can be applied
motor drive, PI controller, complex tuning, to parameter identification and state estimation .
Overshoot, neuro fuzzy controller. The fuzzy logic controller solves the problem of
non-linearities and parameter variations of IM
1 Introduction drive. It achieves high dynamic performance and
Direct torque control(DTC) of induction accurate speed control with good steady-state
motor drives has gained popularity due to its characteristics. The fuzzy rules and membership
simple control structure and sensorless operation. functions are tuned to give better performance.
The structure of conventional DTC drive The proposed neuro fuzzy-based controller has
originally introduced in [1], is simple. It consists been simulated using MATLAB/SIMULINK.
of torque and flux hysteresis-based controllers, The performance of the proposed controller is
flux and torque estimator switching look-up evaluated under various operating conditions.
table. Despite of its simplicity, it was well The simulation results confirm the efficacy of the
known that the implementation of the hysteresis- control system.
based DTC –PI controller requires fine tuning This paper is organized as follows : first
and cannot cope with parameters variation.. This the machine modeling of Induction Motor,
resulted in unpredictable switching frequency of second the direct torque control strategy using PI
the switching devices. Further the variable controller, third the direct torque control strategy
switching frequency will generate unpredictable using neuro fuzzy-based controller, fourth
672 | P a g e
2. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
Simulation results at different operating λqr = Lriqr + Lmiqs (2)
conditions, fifth conclusions.
The electromagnetic torque in the stationary
2.Induction Motor Modelling reference frame is given as
The induction motor model can be
developed from its fundamental electrical and Te = (3/2)(P/2)(λdsi qs −λqsids) (3)
mechanical equations. In the stationary reference
frame the voltage equations are given by
3. DTC with PI Controller
Vds = Rsids + pλds In the DTC scheme [1] (Figure 1), the
Vqs = Rsiqs + pλqs electromagnetic torque and flux signals are
0 = Rr idr + ωr λqr + pλdr delivered to two hysteresis comparators. The
0 = Rr iqr - ωr λdr + pλqr (1) corresponding output variables and the stator
flux position sector are used to select the
Where p indicates the differential appropriate voltage vector from a switching table
operator(d/dt). The stator and rotor flux linkages which generates pulses to control the power
are defined using their respective self leakage switches in the inverter. This scheme presents
inductances and mutual inductances as given many disadvantages (variable switching
below frequency - current and torque distortion caused
by sector changes - start and low-speed operation
λds = Lsids + Lmidr problems ).
λqs = Lsiqs + Lmiqr
λdr = Lridr + Lmids
Figure 1 : Direct Torque Control scheme with PI Controller
All the schemes cited above use a PI
controller for speed control. The use of PI Fuzzy logic and artificial neural
controllers to command a high performance networks can be combined to design a direct
direct torque controlled induction motor drive is torque neuro fuzzy controller. Human expert
often characterised by an overshoot during start knowledge can be used to build an initial
up. This is mainly caused by the fact that the artificial neural network structure whose
high value of the PI gains needed for rapid load parameters could be obtained using online or
disturbance rejection generates a positive high offline learning processes. To eliminate the
torque error. This will let the DTC scheme take above difficulties, a Direct Torque Neuro Fuzzy
control of the motor speed driving it to a value Control scheme (DTNFC) has been proposed.
corresponding to the reference stator flux. At
start up, the PI controller acts only on the error The Neuro Fuzzy inference system
torque value by driving it to the zero border. (ANFIS) is one of the proposed methods to
When this border is crossed, the PI controller combine Fuzzy logic and artificial neural
takes control of the motor speed and drives it to networks[17,21,22]. Figure 2 shows the adaptive
the reference value. NF inference system structure It is composed of
five functional blocks (rule base, database, a
4. DTC with Neuro-fuzzy controller decision making unit, a fuzzyfication interface
673 | P a g e
3. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
and a defuzzyfication interface) which are The block scheme of the proposed self-
generated using four network layers: tuned direct torque neuro-fuzzy controller
Layer 1: This layer is composed of a number of (DTNFC) for a voltage source PWM inverter fed
computing nodes whose activation functions are induction motor is presented in Figure 3. The
fuzzy logic membership functions (usually, internal structure of the NFC is shown in Figure
triangular or bell-shaped functions). 2.
Layer 2: This layer chooses the minimum value
of the inputs. In the first layer of the NF structure,
Layer 3: This layer normalises each input with sampled speed error we ,multiplied by respective
respect to the others (The ith node output is the weights wψ and wT , is mapped through three
ith input divided the sum of all the other inputs). fuzzy logic membership functions. These
Layer 4: This layer’s ith node output is a linear functions are chosen to be triangular shaped as
function of the third layer’s ith node output and shown in Figure 2.The second layer calculates
the ANFIS input signals and sums all the input the minimum of the input signals. The output
signals. The ANFIS structure can be tuned values are normalised in the third layer, to satisfy
automatically by a least-square estimation (for the following relation:
output membership functions) and a back σi = wi / (Σk wk) (4)
propagation algorithm (for output and input
membership functions). where wi and σi are the ith output signal
of the second and third layer respectively. σi is
considered to be the weights.
Figure 2 : Proposed Neuro fuzzy structure Controller
Figure 3 : Triangular membership function sets
674 | P a g e
4. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
Figure 4 : Direct Torque Control scheme with Neuro Fuzzy Controller
The Neuro Fuzzy speed controller inverter which in turn controls the Torque
combines fuzzy logic and artificial neural parameter of Induction motor[12,15,26].
networks to evaluate the reference torque. This .
evaluation is performed using the reference 5. Simulation Results
speed and actual speed errors. This calculated Direct Torque control scheme with PI
error gives the estimated torque value which is controller and Neuro fuzzy controller are
compared with the actual torque value in the implemented in the Induction motor drive with
Hysteresis comparator. The corresponding the following parameters under various operating
torque,flux from hysteresis comparators and conditions at no load and sudden change in load
angle estimated from Flux Torque estimator are applied to the motor. All the results are
given as inputs to Switching table which represented taking time as the x-axis.
generates the appropriate voltage vector for the V=260/50Hz. Rs=0.9Ω Rr=1.227Ω P=2
Ls=150.64mH, Lr=150.64mH. Lm=143.84mH
Figure 5 : Torque Characteristics at no load with PI Controller
675 | P a g e
5. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
Figure 6 : Torque Characteristics with sudden change in load Torque from -6.4Nm to 6.4Nm with PI
Controller
Figure 7 : Speed Characteristics at no load with PI Controller
Figure 8: Speed Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with PI Controller
Figure 9 : Stator flux response with PI controller
676 | P a g e
6. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
Figure 10 : Magnetizing and Torque components of stator current with PI controller
Figure 11: Torque Characteristics at no load with NeuroFuzzy Controller
Figure 12 : Torque Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with NeuroFuzzy
Controller
Figure 13 : Speed Characteristics at no load with NeuroFuzzy Controller
677 | P a g e
7. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
Figure 14 : Speed Characteristics at sudden change in load Torque from -6.4Nm to 6.4Nm with NeuroFuzzy
Controller
Figure 15 :Stator flux response with Neuro fuzzy controller
Figure 16 : Magnetizing and Torque components of stator current with Neuro fuzzy controller
678 | P a g e
8. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
Parameters PI Neuro-fuzzy
Controller Controller
Overshoot 47% 7%
Computational effort 21.6µs 97.2µs
Table I : Comparison between PI Controller and Neuro fuzzy Controller
The overshoot and time for Transcations on Idustrial Electronics, vol
computational effort for PI controller and Neuro 47, no 2,2000, pp 380-388.
fuzzy controller in Direct Torque control of [5] L. Mokrani and R. Abdessemed., (2003)
Induction Motor are estimated. Table I shows A fuzzy self-tuning P1 controller for
that DTNFC scheme gives better performances speed control of induction motor drive,
than the conventional DTC scheme with PI Proceedings of IEEEConference on
controller. We can remark however that the high Control Applications, CCA , vol. 1, 23-25
value of the DTNFC scheme regarding ,2003,pp 785-790.
computational effort does not affect the control [6] W J Wang and J Y Chen., Compositive
cycle since it stays below 50% of its value. adaptive position control of induction
motors based on passivity theory, IEEE
Transactions on Energy Conversion,
6. Conclusions vol.16, no.2,,2001,pp. 180- 185.
In this paper, both Direct torque [7] T C. Chen and T T. Sheu., Model
controlled PI controller and neuro fuzzy reference neural network controller for
Controllers are discussed.The PI controller induction motor speed control" , IEEE
cannot prevent DTC scheme from driving the Transactions on Energy Conversion, vol.
motor speed to the stator flux corresponding 17, no.2,2002, pp.157- 163.
speed. This will most likely result in a speed [8] M. N. Uddin, T S. Radwan and M. A.
overshoot. Simulation of the DTNFC Induction Rahman., Performance of fuzzy logic-
motor drive for speed control shows promising based indirect vector control for induction
results. The motor reaches the reference speed motor drive, IEEE Trans. Ind
rapidly and with minimum overshoot. The Applications, vol. 38, no. 5,
simulation results obtained show the ,2002,pp.1219-1225.
effectiveness of neuro fuzzy controller in speed [9] B. Kosko. Neural Networks and Fuzzy
regulation of induction motor. Systems: A Dynamic Systems Approach
to Machine Intelligence. Englewood
References Cliffs, NJ: Prentice-Hall,1992.
[1] 1. Takahashi and T. Noguchi., A new [10] A. Miloudi, E. A. Alradadi, A. Draou A
quick-response and high-efficiency new control strategy of direct torque fuzzy
control strategy of an induction motor control of a PWM inverterfed induction
IEEE Trans. IndAppl. Vol. IA-22, No, motor drive ”, Conf. Rec. ISIE2006,
5,1986 pp. 820-827. Montreal, CANADA, 09 – 13 ,2006.
[2] E. C. Shin, T S. Park., W. H. Oh and J. Y [11] G. Buja A new control strategy of the
Yoo A design method of PI controller for induction motor drives: The direct flux
an induction motor with parameter and torque control,” IEEE Ind.Electron.
variation. The 29th Annual Conference of Soc. Newslett., vol. 45,1998, pp. 14–16.
the IEEE Industrial Electronics Society, [12] P. Vas Sensorless Vector and Direct
IECON '03.vol 1, 2-6 2003, pp. 408 - 413. Torque Control. Oxford, U.K.: Oxford
[3] C. F. Hu, R. B. Hong, and C. H. Liu., Univ. Press.,1998
Stability analysis and P1 controller tuning [13] D. Casadei, G. Serra, A. Tani,
for a speed-sensorless vector-controlled Implementation of a Direct Torque
induction motor drive 30th Annual Control Algorithm for Induction Motors
Conference of IEEE Industrial Electronics based on Discrete Space Vector
Society, IECON 2004, vol. 1, 2-6, 2004, Modulation IEEE Trans. Power Electron.,
pp.877 - 882. Vol. 15, N◦ 4,2000, pp. 769-777.
[4] B. Robyns, F. Berthereau, J-P. Hautier, [14] P. Z. Grabowski, M. P. Kazmierkowski,
and H. Buyse.,A fuzzy-logic based B. K. Bose, F. Blaabjerg, A Simple Direct
multimodel field orientation in an indirect Torque Neuro Fuzzy Control of PWM
FOC of an induction motor , IEEE Inverter Fed Induction Motor Drive IEEE
679 | P a g e
9. A.Sudhakar*,M.Vijaya Kumar / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, June-July 2012, pp.672-680
Trans. Ind. Electron., Vol. 47, No. 4, [21] J.-S. R. Jang, Self-learning fuzzy controllers
2000,pp. 863-870. based on temporal back propaga-
, Mar./Apr. 1997. tion,IEEE Trans. Neural Networks, vol.
[20] A. Damiano, P. Vas et a[15] M. P. 3,1992, pp. 714–723.
Kazmierko wski, H. Tunia., Automatic [22] J.-S. R. Jang, ANFIS: Adaptive-network-
Control of Converter-Fed Drives. based fuzzy inference system IEEE Trans.
Amsterdam, The Netherlands: Syst., Man, Cybern., vol. 23, 1993, pp.
Elsevier.,1994 665–684.
[16] P. Tiitinen, P. Pohkalainen, J. Lalu, The [23] D. Casadei, G. Grandi, G. Serra, Study
next generation motor control method: and implementation of a simplified and
Direct torque control (DTC),EPE J., vol. efficient digital vector controller for
5, 1995, pp. 14–18. induction motors, in Proc. EMD’9 3,
[17] J.-S. R. Jang, C.-T. Sun, Neuro-fuzzy Oxford, U.K.,1993, pp. 196–201.
modeling and control Proc. IEEE, vol. 83, [24] M. Depenbrok, Direct self-control (DSC)
1995, pp. 378–406. of inverter fed induction machine IEEE
[18] M. P. Kazmierkowski, A. Kasprowicz., Trans. Power Electron., vol. PE-3,1988,
Improved direct torque and flux vector pp. 420–429.
control of PWM inverter-fed induction [25] I. Boldea, S. A. Nasar, Torque vector
motor drives IEEE Trans. Ind. Electron, control (TVC)—A class of fast and robust
vol. 45,1995, pp. 344–350 . torque speed and position digital
[19] J. N. Nash, Direct torque control, induction controller for electric drives in Proc.
motor vector control without an encoder EMPS, vol. 15,1988, pp. 135–148.
IEEE Trans. Ind. [26] T. G. Hableter, F. Profumo, M. Pastorelli,
Applicat., vol. 33, 1997, pp. 333–341l., L. M. Tolbert, Direct torque control of
Comparison of speed-sensorless DTC induction machines using space vector
induction motor drives in Proc. PCI M, modulation IEEE Trans. Ind. Applicat.,
Nuremberg, Germany, pp. 1–11. vol. 28, 1992,pp. 1045–105.
680 | P a g e