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.
Power optimisation scheme of induction motor using FLC for electric vehicleAsoka Technologies
In electric vehicles (EVs) and hybrid EVs, energy efficiency is essential where the energy storage is limited. Adding to its high stability and low cost, the induction motor efficiency improves with loss minimisation. Also, it can consume more power than the actual need to perform its working when it is operating in less than full load condition. This study proposes a control strategy based on the fuzzy logic control (FLC) for EV applications. FLC controller can improve the starting current amplitude and saves more power. Through the MATLAB/SIMULINK software package, the performance of this control was verified through simulation. As compared with the conventional proportional integral derivative controller, the simulation schemes show good, high-performance results in time-domain response and rapid rejection of system-affected disturbance. Therefore, the core losses of the induction motor are greatly reduced, and in this way improves the efficiency of the driving system. Finally, the suggested control system is validated by the experimental results obtained in the authors’ laboratory, which are in good agreement with the simulation results.
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.
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.
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.
Rotor Resistance Adaptation Scheme Using Neural Learning Algorithm for a Fuzz...IOSR Journals
This document presents a rotor resistance adaptation scheme using a neural learning algorithm for a fuzzy logic based sensorless vector control of an induction motor. It proposes using a fuzzy logic controller for speed control of the induction motor drive to provide superior transient performance compared to a PI controller. It also uses a neural network to estimate the rotor resistance online and adapt to variations caused by temperature changes. Simulation results show that the fuzzy logic controller has faster response to speed changes than the PI controller. The neural network is also able to accurately estimate changes in rotor resistance and the adapted system is robust to rotor resistance variations of up to 100%.
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.
This paper introduces experimental comparison study between six and four switch inverter fed three phase induction motor drive system. The control strategy of the drive is based on speed sensoreless vector control using model reference adaptive system as a speed estimator. The adaptive mechanism of speed control loop depends on fuzzy logic control. Four switch inverter conFigureurations reduces the cost of the inverter, the switching losses, the complexity of the control algorithms, interface circuits, the computation of real-time implementation, volume-compactness and reliability of the drive system. The robustness of the proposed model reference adaptive system based on four switch three-phase inverter (FSTPI) fed induction motor drive is verified experimentally at different operating conditions. Experimental work is carried using digital signal processor (DSP1103) for a 1.1 kW motor. A performance comparison of the proposed FSTP inverter fed IM drive with a conventional six switch three-phase inverter (SSTP) inverter system is also made in terms of speed response. The results show that the proposed drive system provides a fast speed response and good disturbance rejection capability. The proposed FSTP inverter fed IM drive is found quite acceptable considering its performance, cost reduction and other advantages features.
Comparison of dc motor speed control performance using fuzzy logic and model ...Mustefa Jibril
1) The document compares the performance of controlling the speed of a DC motor using fuzzy logic and model predictive control (MPC) methods.
2) Simulation results show that the DC motor controlled with a fuzzy logic controller had less overshoot and faster settling time when tracking speed setpoints compared to the motor controlled with an MPC controller.
3) For both square wave and white noise speed setpoints, the MPC controller resulted in higher overshoot and longer settling times than the fuzzy logic controller.
Power optimisation scheme of induction motor using FLC for electric vehicleAsoka Technologies
In electric vehicles (EVs) and hybrid EVs, energy efficiency is essential where the energy storage is limited. Adding to its high stability and low cost, the induction motor efficiency improves with loss minimisation. Also, it can consume more power than the actual need to perform its working when it is operating in less than full load condition. This study proposes a control strategy based on the fuzzy logic control (FLC) for EV applications. FLC controller can improve the starting current amplitude and saves more power. Through the MATLAB/SIMULINK software package, the performance of this control was verified through simulation. As compared with the conventional proportional integral derivative controller, the simulation schemes show good, high-performance results in time-domain response and rapid rejection of system-affected disturbance. Therefore, the core losses of the induction motor are greatly reduced, and in this way improves the efficiency of the driving system. Finally, the suggested control system is validated by the experimental results obtained in the authors’ laboratory, which are in good agreement with the simulation results.
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.
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.
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.
Rotor Resistance Adaptation Scheme Using Neural Learning Algorithm for a Fuzz...IOSR Journals
This document presents a rotor resistance adaptation scheme using a neural learning algorithm for a fuzzy logic based sensorless vector control of an induction motor. It proposes using a fuzzy logic controller for speed control of the induction motor drive to provide superior transient performance compared to a PI controller. It also uses a neural network to estimate the rotor resistance online and adapt to variations caused by temperature changes. Simulation results show that the fuzzy logic controller has faster response to speed changes than the PI controller. The neural network is also able to accurately estimate changes in rotor resistance and the adapted system is robust to rotor resistance variations of up to 100%.
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.
This paper introduces experimental comparison study between six and four switch inverter fed three phase induction motor drive system. The control strategy of the drive is based on speed sensoreless vector control using model reference adaptive system as a speed estimator. The adaptive mechanism of speed control loop depends on fuzzy logic control. Four switch inverter conFigureurations reduces the cost of the inverter, the switching losses, the complexity of the control algorithms, interface circuits, the computation of real-time implementation, volume-compactness and reliability of the drive system. The robustness of the proposed model reference adaptive system based on four switch three-phase inverter (FSTPI) fed induction motor drive is verified experimentally at different operating conditions. Experimental work is carried using digital signal processor (DSP1103) for a 1.1 kW motor. A performance comparison of the proposed FSTP inverter fed IM drive with a conventional six switch three-phase inverter (SSTP) inverter system is also made in terms of speed response. The results show that the proposed drive system provides a fast speed response and good disturbance rejection capability. The proposed FSTP inverter fed IM drive is found quite acceptable considering its performance, cost reduction and other advantages features.
Comparison of dc motor speed control performance using fuzzy logic and model ...Mustefa Jibril
1) The document compares the performance of controlling the speed of a DC motor using fuzzy logic and model predictive control (MPC) methods.
2) Simulation results show that the DC motor controlled with a fuzzy logic controller had less overshoot and faster settling time when tracking speed setpoints compared to the motor controlled with an MPC controller.
3) For both square wave and white noise speed setpoints, the MPC controller resulted in higher overshoot and longer settling times than the fuzzy logic controller.
This document summarizes a study that uses Takagi-Sugeno fuzzy logic control as a speed controller for indirect field oriented control of an induction motor drive. The study builds a simulation model of indirect field oriented control for an induction motor in MATLAB Simulink. Takagi-Sugeno fuzzy logic is then applied as the speed controller using error and derivative error as inputs and change of torque command as the output. Simulation results show zero overshoot, a rise time of 0.4 seconds, and a settling time of 0.4 seconds. The steady state error is 0.01% indicating the speed can accurately follow the reference speed. The fuzzy logic controller provides effective speed control for the induction motor drive.
This paper presents the comparative performances of Indirect Field Oriented Control (IFOC) for the three-phase induction motor. Recently, the interest of widely used the induction motor at industries because of reliability, ruggedness and almost free in maintenance. Thus, the IFOC scheme is employed to control the speed of induction motor. Therefore, P and PI controllers based on IFOC approach are analyzed at differences speed commands with no load condition. On the other hand, the PI controller is tuned based on Ziegler-Nichols method by using PSIM software which is user-friendly for simulations, design and analysis of motor drive, control loop and the power converter in power electronics studies. Subsequently, the simulated of P controller results are compared with the simulated of PI controller results at difference speed commands with no load condition. Finally, the simulated results of speed controllers are compared with the experimental results in order to explore the performances of speed responses by using IFOC scheme for three-phase induction motor drives.
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
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).
A Flexible Closed Loop PMDC Motor Speed Control System for Precise PositioningWaqas Tariq
The speed control of DC motor is very significant especially in applications where precision is of great importance. Due to its ease of controllability the DC motor is used in many industrial applications requiring variable speed and load characteristics. So the precise speed control of a DC motor is very crucial in industry. In this case microcontrollers play a vital role for the flexible control of DC motors. This current research work investigates the implementation of an ATmega8L microcontroller for the speed control of DC motor fed by a DC chopper. The chopper is driven by a high frequency PWM signal. Controlling the PWM duty cycle is equivalent to controlling the motor terminal voltage, which in turn adjusts directly the motor speed. H-bridge circuit is implemented for the bi-directional control of the motor. A prototype of permanent magnet DC motor speed control system using the microcontroller is developed and tested.
This document compares the use of a PI controller and fuzzy logic controller for speed control of an induction motor. It first provides background on induction motors and common speed control techniques. It then describes the basic structure and advantages of PI controllers and fuzzy logic controllers for motor speed control. Specifically, it details the design of a fuzzy logic controller for this application, including the 49 rule base and membership functions used. Finally, it presents the MATLAB/Simulink models used to simulate and compare the PI controller and fuzzy logic controller for induction motor speed control.
This document analyzes the closed-loop speed control of a chopper-fed separately excited DC motor using PI controllers. It presents the modeling of a separately excited DC motor and discusses various controller types for DC motor speed control. The proposed system uses a buck converter/chopper to control the armature voltage and thereby the speed of the DC motor. PI controllers are used to generate PWM pulses for the chopper by comparing the reference and feedback speeds. Simulation and experimental results are presented to validate the closed-loop speed control using PI controllers for different load conditions.
Neural network based vector control of induction motorcsandit
1) The document presents a study on using a radial basis function neural network (RBFNN) to compensate for drift in the stator currents of an induction motor under vector control. Stator current data with implemented errors was collected to train the RBFNN.
2) An RBFNN with 3 input, 125 hidden, and 3 output nodes trained on 960 data points achieved a root mean square error of 0.34525. When tested, the network was able to restore stator currents close to their original values, validating that it can compensate for drift.
3) Using an RBFNN with k-means clustering to select hidden nodes was able to learn the compensation behavior accurately from data and generalize
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.
T ORQUE R IPPLES M INIMIZATION ON DTC C ONTROLLED I NDUCTION M OTOR WITH A DA...csandit
The conventional direct torque control (DTC) method for induction motors suffers from high torque ripples. This document proposes an adaptive bandwidth approach for the hysteresis controllers in DTC to reduce torque ripples. With the adaptive bandwidth approach, the hysteresis bandwidth is determined by the error values from the previous step for stator flux and torque, rather than using a fixed bandwidth. Simulation results show the proposed adaptive bandwidth DTC (AB-DTC) method can reduce torque ripples by around 60% compared to conventional DTC under both unloaded and loaded motor conditions.
Speed Control of Dual Induction Motor using Fuzzy Controller IOSR Journals
This document describes a study on controlling the speed of dual induction motors using a fuzzy logic controller. It presents the modeling of a dual induction motor system with a single inverter driving both motors. It describes calculating a weighted vector value based on the summation torque and stator current of the motors to control their speeds under unbalanced load conditions. The study implements this control method using a fuzzy logic controller in MATLAB simulations. The results show the fuzzy controller improves speed control of the dual induction motors compared to a PI controller, with less oscillation and faster steady state response.
DTC Scheme for a Four-Switch Inverter-Fed PMBLDC Motor Emulating the Six-Swit...IJRST Journal
The paper deals with the direct torque control (DTC) of brushless DC (BLDC) motor drives fed by four-switch three phase inverters (FSTPI) rather than six-switch inverters (SSTPI) in conventional drives. For any three phase inverter require six switches, but these switches are reduced to four. This reduction of power switches from six to four improves the reliability of the inverter, size of the inverter is reduced and cost of the inverter is also reduces. The FSTPI could be regarded as a reconfigured topology of the SSTPI in case of a switch/leg failure which represents a crucial reliability benefit for many applications especially in electric and hybrid propulsion systems. The DTC of FSTPI-fed BLDC motor drives is treated considering two strategies, such as: 1) DTC-1: a strategy inspired from the one intended to SSTPI-fed BLDC motor drives; 2) DTC-2: a strategy that considers a dedicated vector selection subtable in order to independently control the torques developed by the phases connected to the FSTPI legs during their simultaneous conduction. The operational principle of the four-switch BLDC motor drive and the developed control scheme are theoretically analyzed and the performance is demonstrated by simulation.
Improved Rotor Speed Brushless DC Motor Using Fuzzy Controllerijeei-iaes
This document describes research on improving the rotor speed of a brushless DC motor using a fuzzy controller. It begins with background on BLDC motors and their advantages over brushed DC motors. It then provides the mathematical model of a BLDC motor. Next, it discusses implementing a PID fuzzy controller for a BLDC motor in Simulink and the design procedure, which involves first designing a conventional PID controller, then a linear fuzzy PID controller with the same performance, and finally adjusting the fuzzy inference system to create a nonlinear fuzzy PID controller.
This document compares the performance of indirect vector control of an induction motor using proportional-integral (PI) and proportional-integral-derivative (PID) speed controllers. It first provides background on induction motors, vector control techniques, and PI/PID controllers. It then presents the simulation model and results, which show the PID controller provides better speed response characteristics like shorter settling time. In conclusion, the PID controller improves the speed performance for indirect vector control of an induction motor drive.
This document summarizes a research paper on using fuzzy logic to control the speed of a DC motor. It begins by describing the objectives of designing a high-current driver circuit for the motor and using an efficient fuzzy logic algorithm to accurately track motor velocity. It then provides mathematical analysis of the separately excited DC motor and describes implementing a fuzzy logic controller using FPGA. Membership functions, rule inference, and defuzzification are implemented in VHDL. Feedback is provided by an optical encoder to measure motor speed. Simulation results show the fuzzy controller can accurately control motor speed. In conclusion, the fuzzy logic approach provides an efficient and accurate method for DC motor speed control that does not require an accurate mathematical model of the motor.
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.
This document presents a neural network predictive controller for three phase inverter fed induction motor speed control. A neural network predictive controller is designed to control the motor speed by varying its reference value and regulating the motor speed. Simulation results show the performance of the induction motor drive during step changes in speed and load torque with the neural network predictive controller, with lower current ripple and reduced torque ripple compared to other controllers. The performance of the neural network predictive controller is evaluated using integral absolute error and integral time-weighted absolute error metrics.
Direct Torque Control of a Bldc Motor Based on Computing TechniqueIOSR Journals
This document discusses direct torque control of a brushless DC motor using a fuzzy logic controller. It begins with an introduction to brushless DC motors and their advantages over conventional DC motors. It then discusses the mathematical modeling and operation of brushless DC motors. The document proposes using a fuzzy logic controller for speed control of the brushless DC motor. It describes the components and operation of a fuzzy logic controller, including fuzzification, a knowledge base of rules, fuzzy reasoning, and defuzzification. Simulation results are presented showing the performance of the proposed fuzzy logic controller for brushless DC motor speed control.
Investigation of Artificial Neural Network Based Direct Torque Control for PM...cscpconf
This paper investigates solution for the chronically and the biggest problem of direct torque control scheme: high torque ripple. Otherwise, another main problem faced in direct torque control method is difficulties due to complex algorithm to get high performance control for industrial motors. The purpose of this paper is to simplify the control structure by using artificial neural networks learning abilities and to investigate the affects of this structure on torque performance of motor. For this purpose, two different artificial neural networks have been suggested replacing the optimal switching vector selection and flux sector determination process of conventional direct torque control method. Matlab/Simulink based numerical simulations have been carried out to compare motor performances with conventional control structure and proposed artificial neural network based structure. It has been observed that the dynamic response of motor is faster and torque ripple and the controller complexity of the conventional control system has been reduced with the proposed technique.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
This document compares different fuzzy logic controllers for improving the dynamic response of an indirect vector controlled induction motor drive. It presents a new fuzzy PI controller with scaling factors and evaluates its performance against fuzzy PI and fuzzy MRAC (model reference adaptive control) controllers. Simulation results show that the proposed fuzzy PI with scaling factors has a faster settling time than fuzzy PI, and is less complex than fuzzy MRAC while still providing good parameter insensitivity. The proposed controller provides a compromise between complexity, accuracy and settling time for induction motor applications.
Energy Efficient Control of three-Phase Induction Motorijcoa
This paper presents a review of the developments in the field of efficiency optimization of three-phase induction motor through optimal control and design techniques. Optimal control covers both the broad approaches namely, loss model control (LMC) and search control (SC). Optimal design covers the design modifications of materials and construction in order to optimize efficiency of the motor. The use of Artificial Intelligence (AI) techniques such as artificial neural network (ANN), fuzzy logic, expert systems and nature inspired algorithms (NIA), Genetic algorithm and differential evolution in optimization are also included in this paper.
This document provides an overview of electrical energy conservation in Pakistan. It discusses the importance of efficient electrical energy use given Pakistan's current energy crisis. Key areas discussed include management commitment to conservation, identifying conservation opportunities, implementing energy saving projects, and monitoring progress. Specific technologies and practices highlighted for their energy saving potential include variable frequency drives for motors, lighting system improvements, optimizing motor and transformer sizes, and improving power quality through reducing harmonics. The document emphasizes the large potential for energy savings through effective implementation of energy conservation programs and technologies.
This document summarizes a study that uses Takagi-Sugeno fuzzy logic control as a speed controller for indirect field oriented control of an induction motor drive. The study builds a simulation model of indirect field oriented control for an induction motor in MATLAB Simulink. Takagi-Sugeno fuzzy logic is then applied as the speed controller using error and derivative error as inputs and change of torque command as the output. Simulation results show zero overshoot, a rise time of 0.4 seconds, and a settling time of 0.4 seconds. The steady state error is 0.01% indicating the speed can accurately follow the reference speed. The fuzzy logic controller provides effective speed control for the induction motor drive.
This paper presents the comparative performances of Indirect Field Oriented Control (IFOC) for the three-phase induction motor. Recently, the interest of widely used the induction motor at industries because of reliability, ruggedness and almost free in maintenance. Thus, the IFOC scheme is employed to control the speed of induction motor. Therefore, P and PI controllers based on IFOC approach are analyzed at differences speed commands with no load condition. On the other hand, the PI controller is tuned based on Ziegler-Nichols method by using PSIM software which is user-friendly for simulations, design and analysis of motor drive, control loop and the power converter in power electronics studies. Subsequently, the simulated of P controller results are compared with the simulated of PI controller results at difference speed commands with no load condition. Finally, the simulated results of speed controllers are compared with the experimental results in order to explore the performances of speed responses by using IFOC scheme for three-phase induction motor drives.
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
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).
A Flexible Closed Loop PMDC Motor Speed Control System for Precise PositioningWaqas Tariq
The speed control of DC motor is very significant especially in applications where precision is of great importance. Due to its ease of controllability the DC motor is used in many industrial applications requiring variable speed and load characteristics. So the precise speed control of a DC motor is very crucial in industry. In this case microcontrollers play a vital role for the flexible control of DC motors. This current research work investigates the implementation of an ATmega8L microcontroller for the speed control of DC motor fed by a DC chopper. The chopper is driven by a high frequency PWM signal. Controlling the PWM duty cycle is equivalent to controlling the motor terminal voltage, which in turn adjusts directly the motor speed. H-bridge circuit is implemented for the bi-directional control of the motor. A prototype of permanent magnet DC motor speed control system using the microcontroller is developed and tested.
This document compares the use of a PI controller and fuzzy logic controller for speed control of an induction motor. It first provides background on induction motors and common speed control techniques. It then describes the basic structure and advantages of PI controllers and fuzzy logic controllers for motor speed control. Specifically, it details the design of a fuzzy logic controller for this application, including the 49 rule base and membership functions used. Finally, it presents the MATLAB/Simulink models used to simulate and compare the PI controller and fuzzy logic controller for induction motor speed control.
This document analyzes the closed-loop speed control of a chopper-fed separately excited DC motor using PI controllers. It presents the modeling of a separately excited DC motor and discusses various controller types for DC motor speed control. The proposed system uses a buck converter/chopper to control the armature voltage and thereby the speed of the DC motor. PI controllers are used to generate PWM pulses for the chopper by comparing the reference and feedback speeds. Simulation and experimental results are presented to validate the closed-loop speed control using PI controllers for different load conditions.
Neural network based vector control of induction motorcsandit
1) The document presents a study on using a radial basis function neural network (RBFNN) to compensate for drift in the stator currents of an induction motor under vector control. Stator current data with implemented errors was collected to train the RBFNN.
2) An RBFNN with 3 input, 125 hidden, and 3 output nodes trained on 960 data points achieved a root mean square error of 0.34525. When tested, the network was able to restore stator currents close to their original values, validating that it can compensate for drift.
3) Using an RBFNN with k-means clustering to select hidden nodes was able to learn the compensation behavior accurately from data and generalize
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.
T ORQUE R IPPLES M INIMIZATION ON DTC C ONTROLLED I NDUCTION M OTOR WITH A DA...csandit
The conventional direct torque control (DTC) method for induction motors suffers from high torque ripples. This document proposes an adaptive bandwidth approach for the hysteresis controllers in DTC to reduce torque ripples. With the adaptive bandwidth approach, the hysteresis bandwidth is determined by the error values from the previous step for stator flux and torque, rather than using a fixed bandwidth. Simulation results show the proposed adaptive bandwidth DTC (AB-DTC) method can reduce torque ripples by around 60% compared to conventional DTC under both unloaded and loaded motor conditions.
Speed Control of Dual Induction Motor using Fuzzy Controller IOSR Journals
This document describes a study on controlling the speed of dual induction motors using a fuzzy logic controller. It presents the modeling of a dual induction motor system with a single inverter driving both motors. It describes calculating a weighted vector value based on the summation torque and stator current of the motors to control their speeds under unbalanced load conditions. The study implements this control method using a fuzzy logic controller in MATLAB simulations. The results show the fuzzy controller improves speed control of the dual induction motors compared to a PI controller, with less oscillation and faster steady state response.
DTC Scheme for a Four-Switch Inverter-Fed PMBLDC Motor Emulating the Six-Swit...IJRST Journal
The paper deals with the direct torque control (DTC) of brushless DC (BLDC) motor drives fed by four-switch three phase inverters (FSTPI) rather than six-switch inverters (SSTPI) in conventional drives. For any three phase inverter require six switches, but these switches are reduced to four. This reduction of power switches from six to four improves the reliability of the inverter, size of the inverter is reduced and cost of the inverter is also reduces. The FSTPI could be regarded as a reconfigured topology of the SSTPI in case of a switch/leg failure which represents a crucial reliability benefit for many applications especially in electric and hybrid propulsion systems. The DTC of FSTPI-fed BLDC motor drives is treated considering two strategies, such as: 1) DTC-1: a strategy inspired from the one intended to SSTPI-fed BLDC motor drives; 2) DTC-2: a strategy that considers a dedicated vector selection subtable in order to independently control the torques developed by the phases connected to the FSTPI legs during their simultaneous conduction. The operational principle of the four-switch BLDC motor drive and the developed control scheme are theoretically analyzed and the performance is demonstrated by simulation.
Improved Rotor Speed Brushless DC Motor Using Fuzzy Controllerijeei-iaes
This document describes research on improving the rotor speed of a brushless DC motor using a fuzzy controller. It begins with background on BLDC motors and their advantages over brushed DC motors. It then provides the mathematical model of a BLDC motor. Next, it discusses implementing a PID fuzzy controller for a BLDC motor in Simulink and the design procedure, which involves first designing a conventional PID controller, then a linear fuzzy PID controller with the same performance, and finally adjusting the fuzzy inference system to create a nonlinear fuzzy PID controller.
This document compares the performance of indirect vector control of an induction motor using proportional-integral (PI) and proportional-integral-derivative (PID) speed controllers. It first provides background on induction motors, vector control techniques, and PI/PID controllers. It then presents the simulation model and results, which show the PID controller provides better speed response characteristics like shorter settling time. In conclusion, the PID controller improves the speed performance for indirect vector control of an induction motor drive.
This document summarizes a research paper on using fuzzy logic to control the speed of a DC motor. It begins by describing the objectives of designing a high-current driver circuit for the motor and using an efficient fuzzy logic algorithm to accurately track motor velocity. It then provides mathematical analysis of the separately excited DC motor and describes implementing a fuzzy logic controller using FPGA. Membership functions, rule inference, and defuzzification are implemented in VHDL. Feedback is provided by an optical encoder to measure motor speed. Simulation results show the fuzzy controller can accurately control motor speed. In conclusion, the fuzzy logic approach provides an efficient and accurate method for DC motor speed control that does not require an accurate mathematical model of the motor.
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.
This document presents a neural network predictive controller for three phase inverter fed induction motor speed control. A neural network predictive controller is designed to control the motor speed by varying its reference value and regulating the motor speed. Simulation results show the performance of the induction motor drive during step changes in speed and load torque with the neural network predictive controller, with lower current ripple and reduced torque ripple compared to other controllers. The performance of the neural network predictive controller is evaluated using integral absolute error and integral time-weighted absolute error metrics.
Direct Torque Control of a Bldc Motor Based on Computing TechniqueIOSR Journals
This document discusses direct torque control of a brushless DC motor using a fuzzy logic controller. It begins with an introduction to brushless DC motors and their advantages over conventional DC motors. It then discusses the mathematical modeling and operation of brushless DC motors. The document proposes using a fuzzy logic controller for speed control of the brushless DC motor. It describes the components and operation of a fuzzy logic controller, including fuzzification, a knowledge base of rules, fuzzy reasoning, and defuzzification. Simulation results are presented showing the performance of the proposed fuzzy logic controller for brushless DC motor speed control.
Investigation of Artificial Neural Network Based Direct Torque Control for PM...cscpconf
This paper investigates solution for the chronically and the biggest problem of direct torque control scheme: high torque ripple. Otherwise, another main problem faced in direct torque control method is difficulties due to complex algorithm to get high performance control for industrial motors. The purpose of this paper is to simplify the control structure by using artificial neural networks learning abilities and to investigate the affects of this structure on torque performance of motor. For this purpose, two different artificial neural networks have been suggested replacing the optimal switching vector selection and flux sector determination process of conventional direct torque control method. Matlab/Simulink based numerical simulations have been carried out to compare motor performances with conventional control structure and proposed artificial neural network based structure. It has been observed that the dynamic response of motor is faster and torque ripple and the controller complexity of the conventional control system has been reduced with the proposed technique.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
This document compares different fuzzy logic controllers for improving the dynamic response of an indirect vector controlled induction motor drive. It presents a new fuzzy PI controller with scaling factors and evaluates its performance against fuzzy PI and fuzzy MRAC (model reference adaptive control) controllers. Simulation results show that the proposed fuzzy PI with scaling factors has a faster settling time than fuzzy PI, and is less complex than fuzzy MRAC while still providing good parameter insensitivity. The proposed controller provides a compromise between complexity, accuracy and settling time for induction motor applications.
Energy Efficient Control of three-Phase Induction Motorijcoa
This paper presents a review of the developments in the field of efficiency optimization of three-phase induction motor through optimal control and design techniques. Optimal control covers both the broad approaches namely, loss model control (LMC) and search control (SC). Optimal design covers the design modifications of materials and construction in order to optimize efficiency of the motor. The use of Artificial Intelligence (AI) techniques such as artificial neural network (ANN), fuzzy logic, expert systems and nature inspired algorithms (NIA), Genetic algorithm and differential evolution in optimization are also included in this paper.
This document provides an overview of electrical energy conservation in Pakistan. It discusses the importance of efficient electrical energy use given Pakistan's current energy crisis. Key areas discussed include management commitment to conservation, identifying conservation opportunities, implementing energy saving projects, and monitoring progress. Specific technologies and practices highlighted for their energy saving potential include variable frequency drives for motors, lighting system improvements, optimizing motor and transformer sizes, and improving power quality through reducing harmonics. The document emphasizes the large potential for energy savings through effective implementation of energy conservation programs and technologies.
This document discusses using an artificial neural network (ANN) for speed and torque control of a three-phase induction motor. It proposes a new ANN-based method for flux position estimation, sector selection, and voltage vector selection for direct torque control of induction motors. Simulation and experimental results show the ANN-based approach reduces torque and flux ripples compared to conventional direct torque control. The ANN-based speed controller also achieves good dynamic performance of the induction motor drive. Results indicate the ANN-based direct torque control scheme has better performance than the modified direct torque control scheme.
Induction motor fault diagnosis by motor current signature analysis and neura...Editor Jacotech
This paper presents steps in designing dimensions of antenna and feed in axisymmetrical Cassegrain Antennas. Corrugated conical horns are used as feeds in applications with circular polarization. In the initial design steps, diameter of the main reflector is determined considering communication link budget, then feed dimensions are designed to create optimum aperture efficiency. In the last step of design, considering feed dimensions and the amount of its center phase displacements with respect to its aperture plane, dimensions of subreflector is determined to avoid additional blockage. To illustrate the procedure, an applicative design example is presented in X-band.
Speed Control of Induction Motor using Variable Frequency DriveSandeep Kaushal
Induction motor is constant speed motor at a particular frequency and consumes almost same power irrespective of load demand. Let's talk about two different load one is high load and other low load. AT low load motor is delivering the load with some current and thereby torque is maintained. If load goes high, to maintain the same speed and developed torqued, motor will draw extra current and will corresponds to more losses. If speed of motor is reduced corresponding too low load and is increased corresponding to high load, then substantial amount of power can be saved. And speed can be changed by changing the frequency of input supply.
AC Induction motor (IM) are used as actuators in many industrial processes. Although IMs are reliable, they are subjected to some undesirable stresses, causing faults resulting in failure. Monitoring of an IM is a fast emerging technology for the detection of initial faults. It avoids unexpected failure of an industrial process. Monitoring techniques can be classified as the conventional and the digital techniques.
1.1 PROTECTION SCHEME OF INDUCTION MOTOR
Classical monitoring techniques for three-phase IMs are generally provided by some combination of mechanical and electrical monitoring equipment. Mechanical forms of motor sensing are also limited in ability to detect electrical faults, such as stator insulation failures. In addition, the mechanical parts of the equipment can cause problems in the course of operation and can reduce the life and efficiency of a system.
It is well known that IM monitoring has been studied by many researchers and reviewed in a number of works. Reviews about various stator faults and their causes, and detection techniques, latest trends, and diagnosis methods supported by the artificial intelligence, the microprocessor, the computer and other techniques in monitoring unbalanced voltage inter turn faults, stator winding temperature and microcontroller based digital protectors have been recently studied subjects. In these, while one or two variables were considered together to protect the IMs, the variables of the motor were not considered altogether. Measurements of the voltages, currents, temperatures, and speed were achieved and transferred to the computer for final protection decision.
A programmable integrated circuit (PIC) based protection system has been introduced using Microprocessors and the solutions of various faults of the phase currents, the phase voltages, the speed, and the winding temperatures of an IM occurring in operation have been achieved with the help of the microcontroller, but these electrical parameters have not been displayed on a screen.
Nowadays, the most widely used area of programmable logic controller (PLC) is the control circuits of industrial automation systems. The PLC systems are equipped with special I/O units appropriate for direct usage in industrial automation systems. The input components, such as the pressure, the level, and the temperature sensors, can be directly connected to the input. The driver components of the control circuit such as contactors and solenoid valves can directly be connected to the output.
speed control of three phase induction motorAshvani Shukla
This document summarizes various methods for controlling the speed of three-phase induction motors. It discusses that induction motors are commonly used in industry due to their low cost and rugged construction but operate at constant speed. Various speed control methods are then outlined, including stator voltage control, stator frequency control, and stator current control. V/F control is also explained in detail along with its advantages for providing efficient motor speed control. The document concludes by discussing applications in industry and topics for further research.
This document describes a smart energy meter that uses a GSM module to send electricity consumption data via SMS. The meter uses an AD7751 IC to measure real power consumption based on current and voltage inputs. An AVR microcontroller then processes this data and calculates energy used. It can send meter readings, billing information, and load details to the user's mobile phone upon request via a missed call to provide real-time monitoring. The smart meter allows for accurate and automated energy monitoring and billing compared to traditional meters.
LIGHTWEIGHT MOBILE WEB SERVICE PROVISIONING FOR THE INTERNET OF THINGS MEDIATIONijujournal
Emerging sensor-embedded smartphones motivated the mobile Internet of Things research. With the
integrated embedded hardware and software sensor components, and mobile network technologies,
smartphones are capable of providing various environmental context information via embedded mobile
device-hosted Web services (MWS). MWS enhances the capability of various mobile sensing applications
such as mobile crowdsensing, real time mobile health monitoring, mobile social network in proximity and
so on. Although recent smartphones are quite capable in terms of mobile data transmission speed and
computation power, the frequent usage of high performance multi-core mobile CPU and the high speed
3G/4G mobile Internet data transmission will quickly drain the battery power of the mobile device.
Although numerous previous researchers have tried to overcome the resource intensive issues in mobile
embedded service provisioning domain, most of the efforts were constrained because of the underlying
resource intensive technologies. This paper presents a lightweight mobile Web service provisioning
framework for mobile sensing which utilises the protocols that were designed for constrained Internet of
Things environment. The prototype experimental results show that the proposed framework can provide
higher throughput and less resource consumption than the traditional mobile Web service frameworks.
SPEED AND TORQUE CONTROL OF AN INDUCTION MOTOR WITH ANN BASED DTCijics
Due to advantages such as fast dynamic response, simple and robust control structure, direct torque
control (DTC) is commonly used method in high performance control method for induction motors. Despite
mentioned advantages, there are some chronically disadvantages with this method like high torque and
current ripples, variable switching behaviour and control problems at low speed rates. On the other hand,
artificial neural network (ANN) based control algorithms are getting increasingly popular in recent years
due to their positive contribution to the system performance. The purpose of this paper is investigating of
the effects of ANN integrated DTC method on induction motor performance by numerical simulations. For
this purpose, two different ANN models have been designed, trained and implemented for the same DTC
model. The first ANN model was designed to select optimum inverter and the second model was designed to
use in the determination of the flux vector position. Matlab/Simulink model of the proposed ANN based
DTC method was created in order to compare with the conventional DTC and the proposed DTC methods.
The simulation studies proved that the induction motor torque ripples have been reduced remarkably with
the proposed method and this approach can be a good alternative to the conventional DTC method for
induction motor control.
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.
IRJET-Investigation of Three Phase Induction Speed Control Strategies using N...IRJET Journal
This document discusses speed control strategies for a three-phase induction motor using different controllers. It first introduces induction motors and their challenges in speed control due to their complex nonlinear dynamics. It then discusses various speed control techniques including proportional (P), proportional-integral (PI) and proportional-integral-derivative (PID) control. The document presents MATLAB simulations of speed control using these different controllers under full load conditions. The results show that PID control provides more efficient speed control with minimal steady state error and faster response compared to no controller or only P control. Vector control techniques are used to implement the speed controllers in the simulation.
OPTIMAL TORQUE RIPPLE CONTROL OF ASYNCHRONOUS DRIVE USING INTELLIGENT CONTROL...elelijjournal
This document summarizes a research paper on optimizing torque ripple in an asynchronous drive using intelligent controllers. It describes using a hybrid controller combining PI and fuzzy logic control with direct torque control and space vector modulation of a three-level cascaded inverter to power an induction motor drive. Simulation results showed the hybrid controller approach minimized torque ripple compared to using just PI or fuzzy logic control alone under no-load and loaded conditions. The optimized torque control method provides benefits for general purpose induction motor applications.
This document describes a study on modeling and simulating a permanent magnet brushless DC (PMBLDC) motor drive system in MATLAB/Simulink. The authors develop a model of a PMBLDC motor with a 120-degree inverter system and implement closed-loop speed control using a PI controller. Simulation results confirm the validity of the model and controller. The paper examines the effects of load and inertia changes on the motor's speed performance.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a journal article that proposes a fuzzy logic approach for sensorless vector control of an induction motor using an efficiency optimization technique. It presents the following:
1) A dynamic model and state space model of the induction motor in a synchronous reference frame for vector control without sensors.
2) A fuzzy logic based online efficiency optimization controller that interfaces with the drive system to minimize power consumption.
3) The controller decrements the flux in steps until the measured input power is minimized. Membership functions and rules for the fuzzy controller are provided.
4) Performance of the drive is analyzed with and without the fuzzy controller using MATLAB/Simulink simulations. The fuzzy approach is found to improve efficiency
CONTROL OF AN INDUCTION MOTOR WITH DOUBLE ANN MODEL BASED DTCcsandit
Direct torque control (DTC) is preferably control method on high performance control of induction motors due to its dvantages such as fast dynamic response, simple and robust control structure. However, high torque and current ripples are mostly faced problems in this control method. This paper presents artificial neural network (ANN) based approach to the DTC method to overcome mentioned problems. In the study, by taking a different perspective to ANN and DTC integration, two different ANN models have been designed, trained and implemented. The first ANN model has been used for switch selecting process and the second one has been used for sector determine process. Matlab/Simulink model of the proposed ANN based DTC method has created in order to compare with the conventional DTC and the proposed DTC
methods. The simulation studies have proved that the induction motor torque and current
ripples have been reduced remarkably with the proposed method and this approach can be a
good alternative to the conventional DTC method for induction motor control
This document describes using a fuzzy logic controller to control the speed of a brushless DC motor. It begins with an abstract that outlines using a fuzzy logic algorithm to track speed references and stabilize output speed during load variations for a BLDC motor drive. It then provides background on BLDC motors and fuzzy logic control. Sections describe the operating principle and components of BLDC motors, including rotor position sensors. It also covers designing a proportional-integral (PI) speed controller as well as simulation results for PI control that show the speed and torque performance. Finally, it discusses the structure of a fuzzy logic controller, including the fuzzification and defuzzification components.
Design and implementation of antenna control servo system for satellite grouIAEME Publication
This document summarizes the design and implementation of an antenna control servo system for a satellite ground station. It describes the modeling and analysis of the system both theoretically and experimentally. Key aspects include designing the drive control system for the antenna, integrating drive chains for elevation and azimuth axes, optimizing the system through mathematical modeling and simulation, and testing the operational system by tracking real satellite passes. Both simulation and experimental results showed the system providing stable and accurate antenna positioning to receive satellite data as required.
Simulation of Fuzzy Sliding Mode Controller for Indirect Vector Control of In...ijsrd.com
Because of the low maintenance and robustness induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy Sliding mode controller. The fuzzy-logic with sliding mode speed controller is employed in the outer loop. The performance of Fuzzy Logic control technique has been presented and analyzed in this work. The fuzzy logic controller is found to be a very useful technique to obtain a high performance speed control. The indirect vector controlled induction motor drive involves decoupling of the stator current in to torque and flux producing components. The analysis, design and simulation of the fuzzy logic controller for indirect vector control of induction motor are carried out based on fuzzy set theory. The model is carried out using Matlab/Simulink. The simulation results shows the superiority of fuzzy sliding mode controller in controlling three phase Induction motor with indirect vector control technique.
IRJET- Vector Control of Three Phase Induction MotorIRJET Journal
This document discusses vector control of a three-phase induction motor. Vector control, also called field-oriented control, allows independent control of torque and flux in induction motors, similar to DC motors. The document describes:
1) How vector control works by transforming stator currents into orthogonal d-q components representing flux and torque.
2) The principle of field-oriented control which locks the d-q reference frame to the rotor flux vector for decoupled control of flux and torque.
3) The simulation model built in MATLAB/Simulink to test vector control, including blocks for Clarke/Park transformations, current control, and a PI speed controller.
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.
Brushless Dc Motor Speed Control Using Proportional-Integral And Fuzzy Contro...IOSR Journals
This document summarizes and compares proportional-integral (PI) control and fuzzy logic control for speed regulation of a permanent magnet brushless DC motor. It first provides background on brushless DC motors and reviews previous literature on speed control techniques. It then describes implementing PI control in closed loop simulations, comparing three tuning methods. Fuzzy logic control is also implemented using two inputs (speed and current errors) and simulation results are presented. Both controllers are able to regulate speed under varying load torques and reference speeds, with fuzzy logic control having better performance in terms of overshoot, settling time and speed drop compared to PI control.
Direct torque control of induction motor using space vector modulationIAEME Publication
This document discusses direct torque control of an induction motor using space vector modulation. It begins with introducing direct torque control as an alternative to field oriented control for controlling torque and flux directly and independently. It then provides details on the principles of vector control and direct torque control in stator reference frames. The document describes the modeling of an induction motor and simulations performed in MATLAB to validate the direct torque control approach. The simulations demonstrate control of speed, torque, and flux under different gain settings of the PI controller.
IRJET- Control of DC Motor by Cascaded Control MethodIRJET Journal
This document describes a cascaded control system for controlling the speed of a DC motor. It has two control loops - an outer speed control loop and an inner current control loop. The speed controller determines the target armature current setpoint based on the difference between actual and reference speeds. The current controller then ensures the armature current matches this setpoint, thereby controlling motor speed. Both loops use PI controllers. The methodology section describes implementing this control system in MATLAB/Simulink. Results show the motor speed and current tracks the reference speed and current setpoint well under no load conditions. Limitations around nonlinearity are also discussed.
Modeling & Simulation of PMSM Drives with Fuzzy Logic ControllerIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This document describes a study comparing different speed control methods for a separately excited DC motor using MATLAB simulation. It develops a mathematical model of the DC motor and designs proportional-integral-derivative (PID), internal model control (IMC), and fuzzy logic controllers. It then simulates the performance of each controller and analyzes the step response results. The fuzzy logic controller provided the fastest rise time and lowest overshoot compared to the PID and IMC controllers.
The document describes the design and implementation of a field programmable gate array (FPGA) based speed control system for a brushless direct current (BLDC) motor. It first discusses the motivation and objectives, providing an overview of BLDC motors and advantages of FPGA controllers. It then presents the simulated and experimental setup, which involves a PI speed controller generating PWM signals to control the motor speed through a 3-phase inverter in closed loop. Simulation and experimental results demonstrate that the FPGA-based closed loop controller improves transient and steady-state speed response compared to an open loop configuration.
Similar to Dynamic Simulation of Induction Motor Drive using Neuro Controller (20)
This document summarizes a research paper that proposes using an artificial neural network tuned by a simulated annealing algorithm for real-time credit card fraud detection. The paper describes how simulated annealing can be used to train the weights of a neural network model to classify credit card transactions as fraudulent or non-fraudulent based on attributes of past transactions. The algorithm is tested on a real-world credit card transaction dataset and is found to effectively classify most transactions correctly, though some misclassifications still occur.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
This document provides an overview of vertical handover decision strategies in heterogeneous wireless networks. It begins with an introduction to always best connectivity requirements in next generation networks that allow users to move between different network technologies. It then discusses the key aspects of handover management, including the three phases of initiation, decision, and execution. Various criteria for the handover decision process are described, such as received signal strength, network connection time, available bandwidth, power consumption, cost, security, and user preferences. Different types of handover decision strategies are categorized, including those based on network conditions, user preferences, multiple attributes, fuzzy logic/neural networks, and context awareness. The strategies are analyzed and their advantages/disadvantages compared.
This paper presents the design and performance comparison of a two stage
operational amplifier topology using CMOS and BiCMOS technology. This conventional op
amp circuit was designed by using RF model of BSIM3V3 in 0.6 μm CMOS technology and
0.35 μm BiCMOS technology. Both the op amp circuits were designed and simulated,
analyzed and performance parameters are compared. The performance parameters such as
gain, phase margin, CMRR, PSRR, power consumption etc achieved are compared. Finally,
we conclude the suitability of CMOS technology over BiCMOS technology for low power
RF design.
In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is
used to improve performance of spectrum sensing techniques used for detection of licensed
(Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed
(Secondary) users are combined to take final decision about presence of primary signal. The
mixing techniques used to generate final decision about presence of PU’s signal are also
called as Fusion techniques / rules. The fusion techniques are further classified as data
fusion and decision fusion techniques. In data fusion technique all the secondary users
(SUs) share their raw information of spectrum detection like detected energy or other
statistical information, while in decision fusion technique all the SUs take their local
decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence
of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND
rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized
CSS. In distributed CSS all the SUs share the spectrum detection information with each
other and by mixing the shared information; all the SUs take final decision individually. In
centralized CSS all the SUs send their detected information to a secondary base station /
central unit which combines the shared information and takes final decision. The secondary
base station shares the final decision with all the SUs in the CRN. This paper covers
overview of information fusion methods used for CSS and analysis of decision fusion rules
with simulation results.
This paper analyzes the impact of network scalability on various physical attributes of Zigbee networks. Simulations were conducted using Qualnet to evaluate the performance of the Zigbee physical layer based on energy consumption and throughput. Energy consumption was analyzed for different modulation schemes (ASK, BPSK, OQPSK), network sizes (2-50 nodes), and clear channel assessment modes. The results showed that OQPSK and ASK had lower energy consumption than BPSK. Throughput was highest for OQPSK. While carrier sense had slightly higher throughput than other CCA modes, the energy consumption differences between CCA modes were minor.
This paper gives a brief idea of the moving objects tracking and its application.
In sport it is challenging to track and detect motion of players in video frames. Task
represents optical flow analysis to do motion detection and particle filter to track players
and taking consideration of regions with movement of players in sports video. Optical flow
vector calculation gives motion of players in video frame. This paper presents improved
Luacs Kanade algorithm explained for optical flow computation for large displacement and
more accuracy in motion estimation.
A rapid progress is seen in the field of robotics both in educational and industrial
automation sectors. The Robotics education in particular is gaining technological advances
and providing more learning opportunities. In automotive sector, there is a necessity and
demand to automate daily human activities by robot. With such an advancement and
demand for robotics, the realization of a popular computer game will help students to learn
and acquire skills in the field of robotics. The computer game such as Pacman offers
challenges on both software and hardware fronts. In software, it provides challenges in
developing algorithms for a robot to escape from the pool of attacking robots and to develop
algorithms for multiple ghost robots to attack the Pacman. On the hardware front, it
provides a challenge to integrate various systems to realize the game. This project aims to
demonstrate the pacman game in real world as well as in simulation. For simulation
purpose Player/Stage is used to develop single-client and multi-client architectures. The
multi- client architecture in player/stage uses one global simulation proxy to which all the
robot models are connected. This reduces the overhead to manage multiple robots proxy.
The single-client architecture enables only two robot models to connect to the simulation
proxy. Multi-client approach offers flexibility to add sensors to each port which will be used
distinctly by the client attached to the respective robot. The robots are named as Pacman
and Ghosts, which try to escape and attack respectively. Use of Network Camera has been
done to detect the global positions of the robots and data is shared through inter-process
communication.
In Content-Based Image Retrieval (CBIR) systems, the visual contents of the
images in the database are took out and represented by multi-dimensional characteristic
vectors. A well known CBIR system that retrieves images by unsupervised method known
as cluster based image retrieval system. For enhancing the performance and retrieval rate
of CBIR system, we fuse the visual contents of an image. Recently, we developed two
cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this
paper, we analyzed the performance of the two recommended CBIR systems at different
levels of precision using images of varying sizes and resolutions. We also compared the
performance of the recommended systems with that of the other two existing CBIR systems
namely UFM and CLUE. Experimentally, we find that the recommended systems
outperform the other two existing systems and one recommended system also comparatively
performed better in every resolution of image.
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
available for diagnosis of brain tumor, MRI is the preferred technology which enables the
diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
A Proxy signature scheme enables a proxy signer to sign a message on behalf of
the original signer. In this paper, we propose ECDLP based solution for chen et. al [1]
scheme. We describe efficient and secure Proxy multi signature scheme that satisfy all the
proxy requirements and require only elliptic curve multiplication and elliptic curve addition
which needs less computation overhead compared to modular exponentiations also our
scheme is withstand against original signer forgery and public key substitution attack.
This document proposes a digital watermarking technique using LSB replacement with secret key insertion for enhanced data security. The technique works by inserting a watermark into the least significant bits of pixels in an image. A secret key is also inserted during transmission for additional security. The watermarked image is generated without noticeably impacting image quality. The proposed method was tested on sample images and successfully embedded watermarks while maintaining visual quality. The technique aims to provide copyright protection and authentication of digital images and documents.
Today among various medium of data transmission or storage our sensitive data
are not secured with a third-party, that we used to take help of. Cryptography plays an
important role in securing our data from malicious attack. This paper present a partial
image encryption based on bit-planes permutation using Peter De Jong chaotic map for
secure image transmission and storage. The proposed partial image encryption is a raw data
encryption method where bits of some bit-planes are shuffled among other bit-planes based
on chaotic maps proposed by Peter De Jong. By using the chaotic behavior of the Peter De
Jong map the position of all the bit-planes are permuted. The result of the several
experimental, correlation analysis and sensitivity test shows that the proposed image
encryption scheme provides an efficient and secure way for real-time image encryption and
decryption.
This paper presents a survey of Dependency Analysis of Service Oriented
Architecture (SOA) based systems. SOA presents newer aspects of dependency analysis due
to its different architectural style and programming paradigm. This paper surveys the
previous work taken on dependency analysis of service oriented systems. This study shows
the strengths and weaknesses of current approaches and tools available for dependency
analysis task in context of SOA. The main motivation of this work is to summarize the
recent approaches in this field of research, identify major issue and challenges in
dependency analysis of SOA based systems and motivate further research on this topic.
In this paper, proposed a novel implementation of a Soft-Core system using
micro-blaze processor with virtex-5 FPGA. Till now Hard-Core processors are used in
FPGA processor cores. Hard cores are a fixed gate-level IP functions within the FPGA
fabrics. Now the proposed processor is Soft-Core Processor, this is a microprocessor fully
described in software, usually in an HDL. This can be implemented by using EDK tool. In
this paper, developed a system which is having a micro-blaze processor is the combination
of both hardware & Software. By using this system, user can control and communicate all
the peripherals which are in the supported board by using Xilinx platform to develop an
embedded system. Implementing of Soft-Core process system with different peripherals like
UART interface, SPA flash interface, SRAM interface has to be designed using Xilinx
Embedded Development Kit (EDK) tools.
The article presents a simple algorithm to construct minimum spanning tree and
to find shortest path between pair of vertices in a graph. Our illustration includes the proof
of termination. The complexity analysis and simulation results have also been included.
Wimax technology has reshaped the framework of broadband wireless internet
service. It provides the internet service to unconnected or detached areas such as east South
Africa, rural areas of America and Asia region. Full duplex helpers employed with one of
the relay stations selection and indexing method that is Randomized Distributed Space Time
are used to expand the coverage area of primary Wimax station. The basic problem was
identified at cell edge due to weather conditions (rain, fog), insertion of destruction because
of multiple paths in the same communication channel and due to interference created by
other users in that communication. It is impractical task for the receiver station to decode
the transmitted signal successfully at the cell edges, which increases the high packet loss and
retransmissions. But Wimax is a outstanding technology which is used for improving the
quality of internet service and also it offers various services like Voice over Internet
Protocol, Video conferencing and Multimedia broadcast etc where a little delay in packet
transmission can cause a big loss in the communication. Even setup and initialization of
another Wimax station nearer to each other is not a good alternate, where any mobile
station can easily handover to another base station if it gets a strong signal from other one.
But in rural areas, for few numbers of customers, installation of base station nearer to each
other is costlier task. In this review article, we present a scheme using R-DSTC technique to
choose and select helpers (relay nodes) randomly to expand the coverage area and help to
mobile station as a helper to provide secure communication with base station. In this work,
we use full duplex helpers for better utilization of bandwidth.
Radio Frequency identification (RFID) technology has become emerging
technique for tracking and items identification. Depend upon the function; various RFID
technologies could be used. Drawback of passive RFID technology, associated to the range
of reading tags and assurance in difficult environmental condition, puts boundaries on
performance in the real life situation [1]. To improve the range of reading tags and
assurance, we consider implementing active backscattering tag technology. For making
mobiles of multiple radio standards in 4G network; the Software Defined Radio (SDR)
technology is used. Restrictions in Existing RFID technologies and SDR technology, can be
eliminated by the development and implementation of the Software Defined Radio (SDR)
active backscattering tag compatible with the EPC global UHF Class 1 Generation 2 (Gen2)
RFID standard. Such technology can be used for many of applications and services.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
2. the NNW seems to have greater impact on power electronics & motor drives area that is evident by the
publications in the literature. Since the 1990s, AI-based induction motor drives have received greater
attention. Apart from the control techniques that exist, intelligent control methods, such as fuzzy logic
control, neural network control, genetic algorithm, and expert system, proved to be superior. Artificial
Intelligent Controller (AIC) could be the best controller for Induction Motor control [1-6]. Since the
unknown and unavoidable parameter variations, due to disturbances, saturation and change in temperature
exists; it is often difficult to develop an accurate system mathematical model. High accuracy is not usually of
high importance for most of the induction motor drive. Controllers with fixed parameters cannot provide
these requirements unless unrealistically high gains are used. Therefore, control strategy must be robust and
adaptive. As a result, several control strategies have been developed for induction motor drives within last
two decades. Much research work is in progress in the design of hybrid control schemes. Fuzzy controller
conventionally is totally dependent to memberships and rules, which are based broadly on the intuition of the
designer. This paper tends to show Neuro controller has edge over fuzzy controller. Sugeno fuzzy controller
is used to train the fuzzy system with two inputs and one output [10-12].The performance of fuzzy logic and
artificial neural network based controllers is compared with that of the conventional proportional integral
controller.
II. DYNAMIC MODELING & SIMULATION OF INDUCTION M OTOR DRIVE
The induction motors dynamic behavior can be expressed by voltage and torque which are time varying. The
differential equations that belong to dynamic analysis of induction motor are so sophisticated. Then with the
change of variables the complexity of these equations decrease through movement from poly phase winding
to two phase winding (q-d). In other words, the stator and rotor variables like voltage, current and flux
linkages of an induction machine are transferred to another reference model which remains stationary[1-6].
Figure. 1 d q Model of Induction Motor
In Fig.1 stator inductance is the sum of the stator leakage inductance and magnetizing inductance (Lls = Ls +
Lm), and the rotor inductance is the sum of the rotor leakage inductance and magnetizing inductance (Llr = Lr
+ Lm). From the equivalent circuit of the induction motor in d-q frame, the model equations are derived. The
flux linkages can be achieved as:
1
=
(1)
1
=
=
(2)
(
)
(3)
45
3. 1
=
+
(
)
(4)
By substituting the values of flux linkages in the above equations, the following current equations are
obtained as:
=
(5)
(
=
)
(6)
=
(7)
=
Where
follows:
mq
md
(
)
(8)
are the flux linkages over Lm in the q and d axes. The flux equations are written as
=
+
=
=
(9)
(10)
+
1
I
as:
+
r
=
1
1
+
(11)
1
is related to the torque by the following mechanical dynamic equation
+
+
2
(12)
then r is achievable from above equation, where:
p: number of poles.
J: moment of inertia (kg/m2).
In the previous section, dynamic model of an induction motor is expressed. The model constructed according
to the equations has been simulated by using MATLAB/SIMULINK as shown in Fig.2 in conventional mode
of operation of induction motor. A 3 phase source is applied to conventional model of an induction motor and
the equations are given by:
=
=
=
(
2
)
2
2
(13)
2
3
sin
t+
2
3
(14)
(15)
By using Parks Transformation, voltages are transformed to two phase in the d-q axes, and are applied to
induction motor. In order to obtain the stator and rotor currents of induction motor in two phase, Inverse park
transformation is applied in the last stage [6].
III. F UZYY LOGIC CONTROLLER
The speed of induction motor is adjusted by the fuzzy controller. The following equation is used to represent
the fuzzy triangular membership functions:
46
4. Figure. 2. Simulated Induction Motor Model in Conventional Mode
0
<
( , , , )=
In fig. 3
controller are shown.
(16)
e,
0
e and three scalar values of each triangle that are applied into this
NS
NB
Z
PS
PB
1
-0.1
-0.05
0
NS
NB
0.05
Z
0.1
0.1
PS
PB
1
-1
0
-0.5
0.5
1
1.5
Figure. 3.
e
and e
In Table-I, the fuzzy rules decision implemented into the controller are
yields the following
( )=
, ,
(
,
, ,
)
,
47
(
)
given. The center of area method
(17)
5. u
u value corresponding to the maximum membership degree of the fuzzy set that is
an output from the rule decision table for the rule R i. The conventional simulated induction motor model as
shown in Fig. 2 is modified by adding Fuzzy controller and is shown in Fig. 4. Speed output terminal of
induction motor is applied as an input to fuzzy controller, and in the initial start of induction motor the error
is maximum, so according to fuzzy rules FC produces a crisp value. Then this value will change the
frequency of sine wave in the speed controller. The sine wave is then compared with triangular waveform to
generate the firing signals of IGBTs in the PWM inverters. The frequency of these firing signals also
gradually change, thus increasing the frequency of applied voltage to Induction Motor [12]. As discussed
earlier, the crisp value obtained from Fuzzy Logic Controller is used to change the frequency of gating
signals of PWM inverter. Thus the output AC signals obtained will be variable frequency sine waves. The
sine wave is generated with amplitude, phase and frequency which are supplied through a GUI. Then the
clock signal which is sampling time of simulation is divided by crisp value which is obtained from FLC. So
by placing three sine waves with different phases, one can compare them with triangular waveform and
generate necessary gating signals of PWM inverter. So at the first sampling point the speed is zero and error
is maximum. Then whatever the speed rises, the error will decrease, and the crisp value obtained from FLC
will increase. So, the frequency of sine wave will decrease which will cause IGBTs switched ON and OFF
faster. It will increase the AC supply frequency, and the motor will speed up. The structure of PWM inverter
is shown in Fig 5. The inputs to these blocks are the gating signals which are produced in speed controller
block. The firing signals are applied to IGBT gates that will turn ON and OFF the IGBTs according to the
following logics.
Figure. 4. Fuzzy Control Induction Motor Model
The logics are applied to generate firing signals applied to speed controller block as shown in Fig. 4.The
output of the PWM inverter is shown in Fig. 6. The flow chart of simulation of fuzzy logic controller is
shown in Fig. 7.
48
7. TABLE I. MODIFIED FUZZY R ULE DECISION
e
PB
NS
NS
ZZ
NS
PS
NB
PB
NB
PS
PS
ZZ
NS
NS
NB
ZZ
PS
PS
ZZ
NS
NS
NS
PB
PS
PS
ZZ
NS
NB
e
NB
ZZ
PB
PB
PS
PS
ZZ
IV. NEURO C ONTROLLER
The most important feature of Artificial Neural Networks (ANN) is its ability to learn and improve its
operation using neural network training [7-8]. A neuron is the connecting block between a summer and an
activation function. The mathematical model of a neuron is given by:
y=
w
x + b … … … … … … … … . . (18)
where (x1, x2… xN) are the input signals of the neuron, (w1, w2,… wN) are their corresponding weights and b
is bias
trained by a learning algorithm which performs the adaptation of weights of the network iteratively until the
error between target vectors and the output of the ANN is less than a predefined threshold. The most popular
supervised learning algorithm is back- propagation, which consists of a forward and backward action. In the
forward step, the free parameters of the network are fixed, and the input signals are propagated throughout
the network from the first layer to the last layer. In the forward phase, a mean square error is computed.
( )=
1
((
( )
( )) … … … … … … … . . (19)
where d i is the desired response, yi is the actual output produced by the network in response to the input xi, k
is the iteration number and N is the number of input-output training data. The second step of the backward
phase, the error signal E(k) is propagated throughout the network in the backward direction in order to
perform adjustments upon the free parameters of the network in order to decrease the error E(k) in a
statistical sense[9]. The weights associated with the output layer of the network are therefore updated using
the following formula:
( )
( + 1) =
( )
… … … … .. (20)
( )
where wji is the weight connecting the jth neuron of the output layer to the ith
the constant learning rate. The objective of this NNC is to develop a back propagation algorithm such that the
output of the neural network speed observer can track the target. Fig. 8 depicts the network structure of the
NNC, which indicates that the neural network has three layered network structure. The first is formed with
five neuron inputs
ANN(K+1)),
ANN
ANN
S(K-1),
S (K-2)). The second layer consists of five
neurons. The last one contains one neuron to give the command variation
S(K)). The aim of the
proposed NNC is to compute the command variation based on the future output variation
ANN(K+1)).
Hence, with this structure, a predictive control with integrator has been realised. At time k, the neural
network computes the command variation based on the output at time (k+1), while the later isn’t defined at
ANN(K+1)
ANN(K).The control law is deduced using the
S(K) =
S(K-1) + G
S(K)).
It can be seen that the d axis and q axis voltage equations are coupled by the terms d E and qE . These terms
are considered as disturbances and are cancelled by using the proposed decoupling method. If the decoupling
method is implemented, the flux component equations become
dr = G(s)vds
qr = G(s)vqs
50
8. Figure.7 Flowchart of Fuzzy Simulation Process
Figure. 8 Neural network speed controller
has to be chosen carefully to avoid instability. The proposed Neural network controller is shown in Fig. 9[89].
51
9. V. SIMULATION RESULTS & DISCUSSION
Modeling and simulation of Induction motor in conventional, fuzzy and adaptive neuro fuzzy are done on
MATLAB/SIMULINK. A complete simulation model for inverter fed induction motor drive incorporating
the proposed FLC, adaptive neuro fuzzy controller and Neuro controller has been developed. The dynamic
performance of the proposed Conventional, FLC, and neuro controller based induction motor drive is
investigated. The proposed neuro controller proved to be more superior as compared to
FLC and
Conventional Controller by comparing the response of conventional and FLC based IM drive.The results of
simulation for induction motor with its characteristics are listed in Appendix 'A'. Fig.10, Fig 11 and Fig.12
show the torque–speed characteristics, torque and speed responses of conventional, and FLC and neuro
controller respectively. It appears the rise time drastically decreases when neuro controller is added to
simulation model and both the results are taken in same period of time. In neural network based simulation,
it is apparent from the simulation results shown in Fig. 10(b) and Fig. 10 (c), torque-speed characteristic
converges to zero in less duration of time when compared with conventional Controller and FLC, which is
shown in Fig. 10(a) and Fig 10(b). Neuro controller has no overshoot and settles faster in comparison with
FLC and conventional controller. It is also noted that there is no steady-state error in the speed response
during the operation when neuro controller is activated as shown in Fig.12. In conventional controller,
oscillations occur, whereas in neuro controller and FLC, no oscillations occur in the torque response before it
finally settles down as shown in Fig. 11. Good torque response is obtained with Neuro controller as
compared to conventional, and FLC at all time instants and speed response is better than conventional
controllers and FLC controller. There is a negligible ripple in speed response with neuro fuzzy controller in
comparison with conventional controller, FLC and adaptive neuro controller under dynamic conditions
which are shown in Fig.11. With the neuro controller, speed reaches its steady state value faster as compared
to Conventional and FLC controller as shown in Fig. 12. The speed comparison between the three artificial
intelligent controller is given in Table-II.
Figure. 9 Neuro control of induction motor drive
52
11. At Load Condition:
Induction motor drive with conventional controller speed response has small peak, but in case of fuzzy
controller, neural network speed response, it is quick and smooth response which is shown in Fig.13. Fig.14,
Fig. 15 and Fig. 13 show the waveforms of torque –speed, torque and speed characteristics with four
controllers. Fig.15 shows the speed response with load torque using the conventional, fuzzy and neuro
controller respectively. The time taken by the conventional controlled system to achieve steady state is much
Torque response of Fuzzy Controller
140
120
100
80
60
40
20
0
-20
-40
-60
0
0.5
1
1.5
Time
2
2.5
3
x 10
5
Figure. 11(b) Torque Response of Fuzzy Controller
Torque Characteristics with Neuro Controller
120
100
80
60
40
20
0
-20
-40
-60
0
0.5
1
1.5
Time in Sec
2
2.5
3
x 10
5
Figure. 11(c) Torque Response of Neuro controller
1800
1600
1400
1200
1000
800
600
400
200
0
0
0.5
1
1.5
Time
2
2.5
6
x 10
Figure.12 (a) Speed Response of Conventional Controller
higher than neuro controlled system. The motor speed follows its reference with zero steady-state error and a
fast response using a neuro controller. On the other hand, the conventional controller shows steady-state error
with a high starting current. It is to be noted that the speed response is affected by application of load. This is
the drawback of a conventional controller with load. It is to be noted that the neuro controller gives better
responses in terms of overshoot, steady-state error and fast response when compared with conventional and
fuzzy. It also shows that the neuro controller based drive system can handle the sudden increase in command
speed quickly without overshoot, under- shoot, and steady-state error, whereas the conventional and fuzzy
54
12. controller-based drive system has steady-state error and the response is not as fast as compared to neuro.
Thus, the proposed neuro based drive has been found superior when compared with the conventional
controller and FLC controller based system.
Speed response of Fuzzy Controller
1800
1600
1400
1200
1000
800
600
400
200
0
-200
0
0.5
1
1.5
Time
2
2.5
3
x 10
5
Figure. 12(b) Speed Response of Fuzzy Controller
Speed Characteristics with Neuro Controller
1800
1600
1400
1200
1000
800
600
400
200
0
-200
0
0.5
1
1.5
Time in Sec
2
2.5
3
5
x 10
Figure. 12 (c) Speed Response of Neuro Controller
Torque-Speed Charcteristics with Conventional Controller
20
15
10
5
0
-5
-200
0
200
400
600
800
1000
1200
1400
Speed -RPM
Figure.13 (a) Torque –Speed Characteristics with Conventional Controller
55
1600
14. Torque Cha rcteristics w ith Neuro Controller
140
120
100
80
60
40
20
0
-20
-40
-60
0
0.5
1
1.5
Tim e -Sec
2
2.5
3
5
x 10
Figure. 14 (c) Torque Response of Neuro Controller
Speed Characteristics with Conventional
1600
1400
1200
1000
800
600
400
200
0
-200
0
10
20
30
40
50
60
70
Time-Sec
Figure.15 (a) Speed Response of Conventional Controller
Speed Characteristics with Fuzzy Controller
1800
1600
1400
1200
1000
800
600
400
200
0
-200
0
0. 5
1
1.5
Time-Sec
2
2.5
3
x 10
5
Figure.15 (b) Speed Response of Fuzzy Controller
Speed Characteristics with Neuro Controller
1800
1600
1400
1200
1000
800
600
400
200
0
-200
0
0.5
1
1.5
Time Sec
2
2.5
Figure.15 (c) Speed Response of Neuro Controller
57
3
5
x 10
15. TABLE-II. SPEED COMPARISON BETWEEN C ONVENTIONAL, FLC AND NN
Speed
Speed in
conventional
Simulation (rpm)
Speed in FLC
based simulation
(rpm)
Time
line
Speed in
ANN
controller
based
simulation
(rpm)
410
0.5
65
400
1
150
800
915
2
240
1680
1710
4
580
1710
1710
8
1460
1710
1710
10
1640
1710
1710
VI. CONCLUSION
In this paper, comparison of simulation results of the induction motor are presented with different types of
controller such as conventional, fuzzy and neuro controller. From the speed waveforms, it is observed that
with neuro controller the rise time decreases drastically, in the manner which the frequency of sine waves are
changing according to the percentage of error from favourite speed. The frequency of these firing signals also
gradually changes, thus increasing the frequency of applied voltage to Induction Motor. According to the
direct relation of induction motor speed and frequency of supplied voltage, the speed will also increase. With
results obtained from simulation, it is clear that for the same operation condition of induction motor, fuzzy
controller has better performance than the conventional controller. By comparing neuro controller with FLC
model, it is apparent that by adding learning algorithm to the control system will decrease the rising time
more than expectation and it proves neuro controller has better dynamic performance as compared to FLC
and conventional controller.
APPENDIX A INDUCTION MOTOR PARAMETERS
The following parameters of the induction motor are chosen for the simulation studies:
V = 220 f = 60 HP = 3 Rs = 0.435 Rr = 0.816
Xls = 0.754 Xlr = 0.754
Xm = 26.13
J = 0.089 rpm = 1710
p=4
REFERENCES
[1] K. L . Shi, T . F. Chan, Y. K. Wong and S. L . HO, "Modeling and simulation of the three phase induction motor
Using SIMULINK," Int.J. Elect. Engg. Educ., Vol. 36, 1999, pp. 163–172.
[2] Tze Fun Chan and Keli Shi, "Applied intelligent control of induction motor drives," IEEE Willey Press, First
edition, 2011.
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