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.
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.
A High Gain Observer Based Sensorless Nonlinear Control of Induction MachineIJPEDS-IAES
In this paper a sensorless Backstepping control scheme for rotor speed and flux control of induction motor drive is proposed. The most interesting feature of this technique is to deal with non-linearity of high-order system by using a virtual control variable to render the system simple. In this technique, the control outputs can be derived step by step through appropriate Lyapunov functions. A high gain observer is performed to estimate non available rotor speed and flux measurements to design the full control scheme of the considered induction motor drive. Simulation results are presented to validate the effectiveness of the proposed sensorless Backstepping control of the considered induction motor.
Fuzzy controlled dtc fed by a four switch inverter for induction motoreSAT Journals
Abstract
Direct Torque Control of induction motor fed drives has become popular and widely used in industries due to fast and good
torque response. Induction motors (IM) are simple in construction and are less sensitive to the motor parameters compared to
other vector control methods. The conventional DTC is based on flux and torque hysteresis controllers. Induction motor is fed
from a Four Switch Inverter generating the voltage vectors of the Six Switch Inverter by reconfiguration. Applying the most
optimized voltage vector that produce fastest dynamic torque response during transient states. Fuzzy logic concept is a most
efficient artificial integilence method which has high application in electric motor drives. A method to achieve fastest dynamic
performance by modifying the two leg inverter fed DTC of induction motor based on Fuzzy Logic Concept is used here. This paper
presents a rule-based fuzzy logic controller scheme designed and applied for the speed control of an induction motor fed from a
four switch three phase inverter emulating the six switch three phase inverter. Due to the usage of the Fuzzy logic concept, the
reliability, efficiency and performance of ac drive increases. Initial torque peak and torque ripple are minimized in the four switch
three phase inverter based DTC using Fuzzy Logic.
Key Words: Direct Torque Control , Four Switch/Six Switch Three Phase Inverter, Fuzzy Logic, Induction motor(IM).
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.
A High Gain Observer Based Sensorless Nonlinear Control of Induction MachineIJPEDS-IAES
In this paper a sensorless Backstepping control scheme for rotor speed and flux control of induction motor drive is proposed. The most interesting feature of this technique is to deal with non-linearity of high-order system by using a virtual control variable to render the system simple. In this technique, the control outputs can be derived step by step through appropriate Lyapunov functions. A high gain observer is performed to estimate non available rotor speed and flux measurements to design the full control scheme of the considered induction motor drive. Simulation results are presented to validate the effectiveness of the proposed sensorless Backstepping control of the considered induction motor.
Fuzzy controlled dtc fed by a four switch inverter for induction motoreSAT Journals
Abstract
Direct Torque Control of induction motor fed drives has become popular and widely used in industries due to fast and good
torque response. Induction motors (IM) are simple in construction and are less sensitive to the motor parameters compared to
other vector control methods. The conventional DTC is based on flux and torque hysteresis controllers. Induction motor is fed
from a Four Switch Inverter generating the voltage vectors of the Six Switch Inverter by reconfiguration. Applying the most
optimized voltage vector that produce fastest dynamic torque response during transient states. Fuzzy logic concept is a most
efficient artificial integilence method which has high application in electric motor drives. A method to achieve fastest dynamic
performance by modifying the two leg inverter fed DTC of induction motor based on Fuzzy Logic Concept is used here. This paper
presents a rule-based fuzzy logic controller scheme designed and applied for the speed control of an induction motor fed from a
four switch three phase inverter emulating the six switch three phase inverter. Due to the usage of the Fuzzy logic concept, the
reliability, efficiency and performance of ac drive increases. Initial torque peak and torque ripple are minimized in the four switch
three phase inverter based DTC using Fuzzy Logic.
Key Words: Direct Torque Control , Four Switch/Six Switch Three Phase Inverter, Fuzzy Logic, Induction motor(IM).
Speed Tracking of Field Oriented Control Permanent Magnet Synchronous Motor U...IJPEDS-IAES
The field oriented control theory and space vector pulse width modulation technique make a permanent magnet synchronous motor can achieve the performance as well as a DC motor. However, due to the nonlinearity of the permanent magnet synchronous motor drive characteristics, it is difficult to control by using conventional proportional-integral-derivative controller. By this reason in this paper an online neural network controller for the permanent magnet synchronous motor is proposed. The controller is designed to tracks variations of speed references and also during load disturbance. The effectiveness of the proposed method is verified by develop simulation model in MATLAB-simulink program. The simulation results show that the proposed controller can reduce the overshoot, settling time and rise time. It can be concluded that the performance of the controller is improved.
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.
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.
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.
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.
Design of Model Free Adaptive Fuzzy Computed Torque Controller for a Nonlinea...Waqas Tariq
In this study, a model free adaptive fuzzy computed torque controller (AFCTC) is designed for a two-degree-of freedom robot manipulator to rich the best performance. Computed torque controller is studied because of its high performance. AFCTC has been also included in this study because of its robust character and high performance. Besides, this control method can be applied to non-linear systems easily. Today, robot manipulators are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools are used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). The strategies of control robot manipulator are classified into two main groups: classical and non-classical methods, however both classical and non-classical theories have been applied successfully in many applications, but they also have some limitation. One of the most important nonlinear robust controller that can used in uncertainty nonlinear systems, are computed torque controller. This paper is focuses on applied non-classical method in robust classical method to reduce the limitations. Therefore adaptive fuzzy computed torque controller will be presented in this paper.
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.
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.
In this paper, the design of a speed control scheme based on a total sliding mode control for Indirect Field Oriented of a three phase induction motor (IM) is proposed. Firstly, the indirect field oriented control is derived. Then, sliding mode control design is investigated to achieve a speed tracking objective under different load torque disturbance. Finally a dSPACE DS1104 R&D board is used to implement the proposed scheme. The experimental results released on 0.25 kW slip-ring IM show a high dynamic performance, fast transient response without overshot as well as a good load disturbances rejection response.
The neural network-based control system of direct current motor driverIJECEIAES
This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on the neural network to control the plant reach the high quality in the case of unknowing the parameters of the plant. The results are that the control quality of the system is very good, the response speed always follows the desired speed and the transition time is small. The simulation results of the neural network control system are shown and compared with that of a PID controller to demonstrate the advantages of the proposed method.
This work treats the modeling and simulation of non-linear system behavior of an induction motor using backstepping sliding mode control (BACK- SMC). First, the direct field oriented control IM is derived. Then, a sliding for direct field oriented control is proposed to compensate the uncertainties, which occur in the control. Finally, the study of Backstepping sliding controls strategy of the induction motor drive. Our non linear system is simulated in MATLAB SIMULINK environment, the results obtained illustrate the efficiency of the proposed control with no overshoot, and the rising time is improved with good disturbances rejections comparing with the classical control law.
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.
Rural electrification by Lakshmi.Nidoni-seminar pptlakshmi nidoni
ABSTRACT
In India, more than 200 million people live in rural areas without access to grid-connected power. A convenient & cost-effective solution would be hybrid power systems which can reduce dependency on grid supply, improve reliability. For a typical domestic load a solar –wind hybrid system is designed with charge controller to charge a conventional battery. To optimize system efficiency, a simple algorithm is developed for system sizing. Total cost of unit is calculated using life cycle cost analysis and payback period.
Speed Tracking of Field Oriented Control Permanent Magnet Synchronous Motor U...IJPEDS-IAES
The field oriented control theory and space vector pulse width modulation technique make a permanent magnet synchronous motor can achieve the performance as well as a DC motor. However, due to the nonlinearity of the permanent magnet synchronous motor drive characteristics, it is difficult to control by using conventional proportional-integral-derivative controller. By this reason in this paper an online neural network controller for the permanent magnet synchronous motor is proposed. The controller is designed to tracks variations of speed references and also during load disturbance. The effectiveness of the proposed method is verified by develop simulation model in MATLAB-simulink program. The simulation results show that the proposed controller can reduce the overshoot, settling time and rise time. It can be concluded that the performance of the controller is improved.
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.
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.
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.
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.
Design of Model Free Adaptive Fuzzy Computed Torque Controller for a Nonlinea...Waqas Tariq
In this study, a model free adaptive fuzzy computed torque controller (AFCTC) is designed for a two-degree-of freedom robot manipulator to rich the best performance. Computed torque controller is studied because of its high performance. AFCTC has been also included in this study because of its robust character and high performance. Besides, this control method can be applied to non-linear systems easily. Today, robot manipulators are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools are used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). The strategies of control robot manipulator are classified into two main groups: classical and non-classical methods, however both classical and non-classical theories have been applied successfully in many applications, but they also have some limitation. One of the most important nonlinear robust controller that can used in uncertainty nonlinear systems, are computed torque controller. This paper is focuses on applied non-classical method in robust classical method to reduce the limitations. Therefore adaptive fuzzy computed torque controller will be presented in this paper.
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.
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.
In this paper, the design of a speed control scheme based on a total sliding mode control for Indirect Field Oriented of a three phase induction motor (IM) is proposed. Firstly, the indirect field oriented control is derived. Then, sliding mode control design is investigated to achieve a speed tracking objective under different load torque disturbance. Finally a dSPACE DS1104 R&D board is used to implement the proposed scheme. The experimental results released on 0.25 kW slip-ring IM show a high dynamic performance, fast transient response without overshot as well as a good load disturbances rejection response.
The neural network-based control system of direct current motor driverIJECEIAES
This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on the neural network to control the plant reach the high quality in the case of unknowing the parameters of the plant. The results are that the control quality of the system is very good, the response speed always follows the desired speed and the transition time is small. The simulation results of the neural network control system are shown and compared with that of a PID controller to demonstrate the advantages of the proposed method.
This work treats the modeling and simulation of non-linear system behavior of an induction motor using backstepping sliding mode control (BACK- SMC). First, the direct field oriented control IM is derived. Then, a sliding for direct field oriented control is proposed to compensate the uncertainties, which occur in the control. Finally, the study of Backstepping sliding controls strategy of the induction motor drive. Our non linear system is simulated in MATLAB SIMULINK environment, the results obtained illustrate the efficiency of the proposed control with no overshoot, and the rising time is improved with good disturbances rejections comparing with the classical control law.
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.
Rural electrification by Lakshmi.Nidoni-seminar pptlakshmi nidoni
ABSTRACT
In India, more than 200 million people live in rural areas without access to grid-connected power. A convenient & cost-effective solution would be hybrid power systems which can reduce dependency on grid supply, improve reliability. For a typical domestic load a solar –wind hybrid system is designed with charge controller to charge a conventional battery. To optimize system efficiency, a simple algorithm is developed for system sizing. Total cost of unit is calculated using life cycle cost analysis and payback period.
A flywheel, in essence is a mechanical battery - simply a mass rotating about an axis.Flywheels store energy mechanically in the form of kinetic energy.They take an electrical input to accelerate the rotor up to speed by using the built-in motor, and return the electrical energy by using this same motor as a generator.Flywheels are one of the most promising technologies for replacing conventional lead acid batteries as energy storage systems.
Speed Control Techniques for Induction Motor - A Reviewijsrd.com
In this paper, various types of speed control methods for the single phase induction motor are described. Speed can be controlled to control the frequency or slip can be controlled to control the torque. Then flux and torque are also function of frequency and voltage. Various methods are used to control the flux and voltage. This paper is focused on sliding mode control technique for induction motor.
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
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.
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.
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.
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.
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
Due to availability of internet and evolution of embedded devices, Internet of things can be useful to contribute in energy domain. The Internet of Things (IoT) will deliver a smarter grid to enable more information and connectivity throughout the infrastructure and to homes. Through the IoT, consumers, manufacturers and utility providers will come across new ways to manage devices and ultimately conserve resources and save money by using smart meters, home gateways, smart plugs and connected appliances. The future smart home, various devices will be able to measure and share their energy consumption, and actively participate in house-wide or building wide energy management systems. This paper discusses the different approaches being taken worldwide to connect the smart grid. Full system solutions can be developed by combining hardware and software to address some of the challenges in building a smarter and more connected smart grid.
A Survey Report on : Security & Challenges in Internet of Thingsijsrd.com
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control of Induction Motor
1. IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 07, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 578
Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control
of Induction Motor
Minaz.M.K1
M.S.Aspalli2
1
M. Tech Student 2
Professor
1,2
Department of Electrical Engineering
1,2
PDA College of Engineering, Gulbarga, Karnataka
Abstract— 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.
Key words: Fuzzy logic, Sliding mode controller, PI
controller, Induction motor drive.
I. INTRODUCTION
The induction motor is considered since its discovery as
actuator privileged in the applications of constant speed, and
it has many advantages, such as low cost, high efficiency,
good self-starting, its simplicity of design, the absence of the
collector brooms system, and a small inertia. However,
induction motor has disadvantages, such as complex,
nonlinear, and multivariable of mathematical model of
induction motor, and the induction motor is not inherently
capable of providing variable speed operation. These
limitations can be solved through the use of smart motor
controllers and adjustable speed controllers, such as scalar
and vector control drive Field Oriented Control (FOC).
vector control was invented in the late 1960. As the
induction motors were controlled using scalar control
methods like the volt hertz control, the magnitude and
frequency of the stator voltages are determined from steady-
state properties of the motor, which leads to poor dynamic
performance. In FOC the magnitude, frequency and
instantaneous position of voltage, current and flux linkage
vector are controlled and valid for steady state as well as
transient conditions. Conventional control of an induction
motor is difficult due to strong nonlinear magnetic
saturation effects and temperature dependency of the
motor’s electrical parameters. As the conventional control
approaches require a complex mathematical model of the
motor to develop controllers for quantities such as speed,
torque, and position. Recently, to avoid the inherent
undesirable characteristics of conventional control
approaches, Fuzzy Logic Controller (FLC) is being
developed. FLC offers a linguistic approach to develop
control algorithms for any system. It maps the input-output
relationship based on human expertise and hence, does not
require an accurate mathematical model of the system and
can handle the nonlinearities that are generally difficult to
model. This consequently makes the FLC tolerant to
parameter variation and more accurate and robust.
The complete paper is demonstrated as follows,
section II describes the indirect vector control of Induction
motor. The design and description of intelligent controllers
are provided in section III the simulation results are
presented in section IV, section V concludes the work.
II. INDIRECT VECTOR CONTROL OF INDUCTION MOTOR
Fig. 2.0: Indirect Vector Control of induction Motor.
Fig.2.0 shows an Indirect Vector Control Method [2]. It
consists of a slip frequency calculation, Inverter Voltage and
Current Elements, sensing integrator of error speed signal
and the corresponding Phase diagram is shown in Fig.2.2.
The Vector control techniques have made possible
the application of induction motors for high-performance
applications, where traditionally only DC drives were
applied. The vector control scheme enables the control of
the induction motor in the same way as separately excitation
DC motors. As in the DC motor, torque control of induction
motor is achieved by controlling the torque current
component and flux current component independently. In
the indirect vector control method, the rotor field angle and
thus the unit vectors are indirectly obtained by summation of
the rotor speed and slip frequency [2].
Fig. 2.1: Block Diagram of Fuzzy Sliding Mode Controller
2. Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control of Induction Motor
(IJSRD/Vol. 2/Issue 07/2014/131)
All rights reserved by www.ijsrd.com 579
For high performance drive the indirect method of
vector control is preferred. The indirect vector control
method is essentially same as the direct vector control
except that the rotor angle θe is generated in an indirect
manner using the measured speed ωr and the slip speed ωsl.
To implement the indirect vector control strategy, it is
necessary to take the following dynamic equations into
consideration [3,4,5]. With respect to phasor diagram of
Indirect Vector Control method of induction motor, this is
shown in Fig.2.2.
Fig. 2.2: Phasor diagram of indirect vector control method
of Induction motor
(2.1)
The rotor equations:
(2.2)
(2.3)
For de-coupling control , So that the flux
directs on the d-axis
Now from equations (2.1) and (2.2), we get
(2.4)
As well, the slip frequency can be calculated as
(2.5)
The slip gain is (2.6)
It is found that the ideal decoupling can be
achieved if the above slip angular speed command is used
for making field orientation. and the constant rotor flux
and =0 can be substituted in equation 2.4,so that the rotor
flux sets as
(2.7)
The electromagnetic torque is given by
(2.8)
Figure 2.1 shows the block diagram of Fuzzy
sliding mode controller for Indirect vector control of
Induction motor The control aim to design a suitable control
law so that the motor speed can track desired speed
commands .[4]
III. DESIGN AND DESCRIPTION OF CONTROLLERS
A. Fuzzy Logic controller
FLC is a technique to embody human-like thinking into a
control system. FLC can be designed to emulate human
deductive thinking, that is, the process people use to infer
conclusions from what they know[6]. The implementation
of offline tuning of PI controller is difficult in dealing with
continuous parametric variation in the induction motor as
well as the nonlinearity present in the entire system.
However, the fuzzy logic based intelligent controller is used
instead of the PI controller; excellent control performance
can be achieved even in the presence of parameter variation
and drive nonlinearity. In addition, the fuzzy logic possesses
the following advantages: (1) The linguistic, not numerical,
variables make the process similar to the human think
process. (2) It relates output to input, without understanding
all the variables, permitting the design of system more
accurate and stable than the conventional control system. (3)
Simplicity allows the solution of previously unsolved
problems. (4) Rapid prototyping is possible because, a
system designer doesn’t have to know everything about the
system before starting work. (5)It has increased robustness.
(6) A few rules encompass great complexity.
Fig 3.0 shows the block diagram of Fuzzy logic
based speed control system. Such a fuzzy logic controller
consists of four basic blocks: Fuzzification, Fuzzy Inference
Engine, Knowledge base and defuzzification.
Fig. 3.0: Fuzzy Interference system
1) Input/ Output variables
The design of the fuzzy logic controller starts with assigning
the input and output variables. The most significant
variables entering the fuzzy logic speed controller has been
selected as the speed error.
2) Fuzzification
In this stage, the crisp variables are converted in to fuzzy
variables respectively. The membership functions associated
to the control variables have been chosen with triangular
shapes in this paper, as shown in figure 3.1(a). The universe
of discourse of all the input and output variables are
established as (-1,1). The suitable scaling factors are chosen
to bring the input variables to this universe of discourse. The
universes of discourse are divided into 3 overlapping fuzzy
sets.
Here the sliding surface S be the input linguistic
variable and fuzzy hitting control law be the output
linguistic variable. The proposed controller uses the
following variables :P (positive),N( negative), Z (zero) for
the input variable S. as shown in figure 1.1.PE (positive
Effort),NE (negative Effort),ZE (zero effort) for the output
variable .The rule base involved in the fuzzy sliding mode
system is given as follows
Rule1: If S is P, then Uf is PE.
Rule2: If S is Z then is ZE.
Rule3: If S is N then Uf is NE.
a b
Fig. 3.1: Membership function (a) Input fuzzy sets (b)
Output fuzzy sets
3. Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control of Induction Motor
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3) Knowledge Base and Inferencing
Knowledge base involves defining the rules represented as
IF-THEN rules statements governing the relationship
between inputs and output variables in terms of membership
functions. Inferencing stage also includes application of
fuzzy operator AND, OR, NOT, implication and
aggregation.
4) Defuzzification
A defuzzification interface, which converts the conclusions
of the inference mechanism into actual inputs for the
process[6]. Hence, In defuzzification stage the fuzzy
variables are converted into a crisp variable. This stage
introduces different inference methods that can be used to
produce the fuzzy set value for the output fuzzy variable U.
B. Sliding mode controller:
Sliding Mode Controller is a Variable Structure Controller
(VSC). Basically, a VSC includes several different
continuous functions that can map plant state to a control
surface, and the switching among different functions is
determined by plant state that is represented by a switching
function [5]. Without lost of generality, consider the design
of a sliding mode controller for the following second order
system: Here we assume b > 0 . u(t) is the input to the
system. The following is a possible choice of the structure of
a sliding mode controller, the system is controlled in such a
way that the tracking error’ and rate of change of
error’ ̇’always move towards a sliding surface. The sliding
surface is defined in the state space by scalar equation.[4]
s (e, ̇,t)=0 Where, the sliding variable, S is
s (t) = e(t) +e(t) (3.1)
(3.2)
Referring to (3.2), the control effort being derived
as the solution of s(t) = 0 without considering the lumped
uncertainty (L(t)=0) is to achieve the desired performance
under nominal model and it is referred to as equivalent
control effort as follows
(3.3)
However, the indirect vector control is highly
parameter sensitive. Unpredictable parameter variatios,
external load disturbance, unmodelled and nonlinear
dynamics adversely affect the control performance of the
drive system. Therefore the control effort cannot ensure the
favorable control performance. Thus auxiliary control effort
should be designed to eliminate the effect of the
unappreciable disturbances.
The auxiliary control effort is referred to as hitting
control effort as:
(3.4)
Where gh is a hitting control gain concerned with
upper bound of uncertainties, and sgn(.) is a sign function.
Now, totally sliding mode control law is as follows
(3.5)
But this controller gives unacceptable performance
due to high control activity, resulting in chattering of control
variable and system states. To reduce chattering a boundary
layer is generally introduced into SMC law, and then the
control law of equation (3.5) can be rewritten as
(3.6)
Where γ is the width of the boundary layer.
Stability inside the layer cannot be ensured and the
inadequate selection of the boundary layer may result in
unstable tracking response. Therefore fuzzy sliding modes
control system, in which a fuzzy logic mechanism is used to
follow the hitting control law.
C. Generalized design of PI controller
The proportional controller is a device that produces an
output signal which is proportional to the input signal. It
improves the steady state tracking accuracy, disturbance
signal rejection and relative stability. It also decreases the
sensitivity of the system to parameter variations. The PI
controller produces an output signal consisting of two terms-
one proportional to input signal and the other proportional to
the integral of input signal [2].
Fig. 3.2: Block diagram of PI Controller
The transfer function of the PI controller is:
(3.7)
Transfer function (PI) = (3.8)
From the figure 3.2, the variable (∆ ) represents
the tracking error, the difference between the desired input
value ( ) and the actual output ( ). This error signal
(∆ ) will be sent to the PI controller, which is shown in
Fig.1.0, and the controller computes the integral of this error
signal. The signal ( ) just past the controller is now equal
to the proportional gain (KP) times the magnitude of the
error plus the integral gain (KI) times error. The output
signal of the controller will be sent to the Vector rotator, and
finally control the Induction motor drive.
IV. MATLAB MODEL OF INDIRECT VECTOR CONTROL OF IM
DRIVE
Fig. 4.0: Matlab Simulink diagram of indirect vector
Control using Fuzzy Sliding Mode Controller
Fig. 4.1: Matlab Simulink diagram of Conversion of abc-dq
4. Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control of Induction Motor
(IJSRD/Vol. 2/Issue 07/2014/131)
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Where,
f(u)=2*(cos(u(4))*u(1)+cos(u(5))*u(2)+cos(u(6))*u(3))/3
Fig. 4.2: Matlab Simulink diagram Figure Sliding mode
Controller
Fig. 4.3: PI- conversion
Fig.4.4: Matlab Simulink diagram of showing Park
transformation
Fig 4.5: Matlab Simulink diagram of SVPWM technique
V. SIMULATION RESULTS
Fig. 4.6: Speed response for periodic command with fuzzy
sliding mode controller
Fig. 4.7: Torque response for periodic command with fuzzy
sliding mode controller
Fig. 4.8:.d-q axis stator currents for periodic command with
fuzzy sliding mode controller
VI. CONCLUSION
This paper has successfully demonstrated the application of
the proposed fuzzy sliding mode control system to an
indirect field-oriented induction motor drive for tracking
periodic commands. The dfaesign and description of the
classical sliding mode controller (SMC) is presented in
detail. Then, the fuzzy logic control is used to mimic the
hitting control law to remove the chattering. However, the
developed fuzzy logic control with indirect vector control of
induction motor drive shows fast response, smooth
performance, and high dynamic response with speed
changing and transient conditions.
REFERENCES
[1] B.K Bose “Modern power electronics and ac drives“
Prentice-Hall Of India, New Delhi,2008.
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controlled method using pi and fuzzy controllers”
International Journal of Science, Environment and
Technology, Vol. 2, No 3, 2013, 457 – 469.
[3] Biranchi Narayan Kar, K.B. Mohanty, Madhu
Singh, Satish choudhury,“Indirect Vector Control of
Induction Motor Using Fuzzy Sliding Mode
Controller”Proc Department of Electrical
Engineering, National Institute of Technology,
Rourkela-769008
[4] Bharat Bhushan, Madhusudan Singh, PremPrakash,
“Performance Analysis of Field Oriented Induction
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Reference Adaptive Control,” International Journal
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[5] Boucheta, I. K. Bousserhane*, A. Hazzab*, P.
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Controller Considering the End Effects” Journal of
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[6] D. Archana, Kotyada. Kalyani, B. Shankar Prasad,
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