Fuzzy logic controller performance in vector control of induction machine
01 Dec 2017-
TL;DR: The study of different topologies of Fuzzy logic based controller for the speed control of induction motors is presented.
Abstract: Fuzzy logic based controllers for various applications have been largely acknowledged due to their robust performance and model-free characteristics. Popularity of induction motors in major industrial applications has inspired the application of Fuzzy logic based controllers in control industrial drives. This paper presents the study of different topologies of Fuzzy logic based controller for the speed control of induction motors. The analysis is being done in MATLAB/SIMULINK environment. The performance of the controllers has been compared with conventional PI controller for different operating conditions and results has been presented.
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03 Jun 2020
TL;DR: This paper presents the modeling, simulation, and emulation of a wind turbine system (WTS) using induction motor (IM) to improve the dynamic behavior of the WTS applying a fuzzy field-oriented controller (FFOC).
Abstract: This paper presents the modeling, simulation, and emulation of a wind turbine system (WTS) using induction motor (IM). The major purpose is to improve the dynamic behavior of the WTS applying a fuzzy field-oriented controller (FFOC). The realization of the control process and the performance of wind turbine are accomplished in detail using MATLAB/Simulink. The achieved simulation results confirm the effectiveness of the utilized control system and the desired performance of the wind turbine emulator (WTE) for giving essential characteristics of the WTS such as rotor speed and electromagnetic torque.
8 citations
Cites background or methods from "Fuzzy logic controller performance ..."
...Block diagram of FOC [9] The mathematical equations for the rotor flux of the considered model is given by:...
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...FLC for speed controller [9]...
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...To calculate electromagnetic torque and rotor flux, stator current is decomposed into a two-dimension d-q system [9]....
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...To control the speed of an IM over a wide range, FOC is used as a suitable vector control method, consisting of two components, one of which is responsible for the flux level and the other controls the torque [9]....
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...The design of an FLC system requires the proper choice of MFs and set the controller to follow these rules accurately [9]....
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01 Feb 2020
TL;DR: The results showed that the hybrid fuzzy-PI controller is better than the classical PI controller in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds.
Abstract: In this paper, a hybrid fuzzy-PI controller has been introduced for induction motor (IM) speed drive. The space vector pulse width modulation technique is used to generate switching pulses for the inverter switching of IGBTs because of low THD for the output voltage. The approach is suitable to attain an optimum trajectory planning for the used hybrid fuzzy-PI to control the speed of the IM. The controller performance is evaluated using MATLAB/Simulink software and will be compared with the case of using classical PI control scheme. The mean absolute error of the IM speed response is used as a fitness function. The actual speed of an IM is compared with a reference speed. The error is given through hybrid fuzzy-PI and classical PI controllers, and their outputs are applied to control the voltage magnitude of the IM. The hybrid fuzzy-PI is also employed to tune and minimize the mean absolute error to enhance the performance of the IM in terms of changes in speed and torque. The results obtained from the hybrid fuzzy-PI control are compared with those obtained through classical PI controller. These results showed that the hybrid fuzzy-PI controller is better than the classical PI controller in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds.
4 citations
Cites background from "Fuzzy logic controller performance ..."
...The issue is to determine a suitable setting of PI coefficients for the slave unit throughout the chaos generation when switching between the chaos control and generation stages [17, 18]....
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29 Mar 2022
TL;DR: In this paper , the input-output feedback linearization technique is used with a fuzzy logic controller to generate the reference voltages for a MC controlled by direct space vector modulation strategy, to achieve a high performance and to ensure a robustness with respect to variations of reference values of speed, torque, and rotor flux.
Abstract: In this paper, The input-output feedback linearization technique is used with a fuzzy logic controller to generate the reference voltages for a MC controlled by direct space vector modulation strategy, to achieve a high performance and to ensure a robustness with respect to variations of reference values of speed, torque, and rotor flux on the one hand, and on the other hand to obtain an operation without injecting harmonics into the grid. Moreover, this paper deals with the damped input filter design of matrix converter. The transfer function and the optimized parameter selection approach of the input filter are also introduced. Finally, in order to examine the effectiveness of the suggested control approach for the drive system considered, a simulation study was carried out.
1 citations
01 Jan 1996
TL;DR: Indirect .field oricntution (IFO) induction .rr~uchar~e drives ure incrcusiny(q erryloyed in .in& drive systems; but the dr+ue perfvr.nr.uuce qjten dey, for the nuxhane porumeter.
Abstract: Indirect .field oricntution (IFO) induction .rr~uchar~e drives ure incrcusiny(q erryloyed in .in& drive systems; but the dr+ue perfvr.nr.uuce qjten dey ,for the nuxhane porumeter. vuriutions. In this puper, U ~f;cLzzy rriwdel reference lcuvruiny control (FMRLC) tech- nique is applied in urL IF0 %nduction much.ine dvLuc; such tht the niuchir~e curb follow (I reference model (UIL per:for.mun ce . Exprrimcntul results urc pes cnt ed to wr-
1 citations
01 Sep 2019
TL;DR: From the results, it can conclude that, the fuzzy-based HCC produce better result compare to FHCC, where the ripple content is reduced and gives better speed performance.
Abstract: Nowadays various control strategies are used to control the Induction Motor(IM). Out of the all controllers Hysteresis Current Controller(HCC) is most popular and very easy to implement. In this paper, the comparison analysis has done between HCC and Fuzzy based HCC. The fuzzy logic controller has used along with the PI controller. The dynamic performance of speed and the ripple content is analyzed using HCC and Fuzzy based HCC. Matlab simulation has been used to get the results with two different strategies: Fixed band Hysteresis current control(FHCC) and Fuzzy based FHCC. The comparison between both the scheme has done in this paper. From the results, we can conclude that, the fuzzy-based HCC produce better result compare to FHCC, where the ripple content is reduced and gives better speed performance.
1 citations
Cites methods from "Fuzzy logic controller performance ..."
...Fuzzy logic is used along with the Speed controller where both PI and fuzzy exist[8]....
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References
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Book•
01 Jan 2015
TL;DR: In this paper, the authors present a simulation of a six-step Thyristor Inverter with three-level Inverters and three-phase Bridge Invergers. And they present a Neural Network in Identification and Control toolbox.
Abstract: (NOTE: Each chapter begins with an Introduction and concludes with a Summary and References.) Preface. List of Principal Symbols. 1. Power Semiconductor Devices. Diodes. Thyristors. Triacs. Gate Turn-Off Thyristors (GTOs). Bipolar Power or Junction Transistors (BPTs or BJTs). Power MOSFETs. Static Induction Transistors (SITs). Insulated Gate Bipolar Transistors (IGBTs). MOS-Controlled Thyristors (MCTs). Integrated Gate-Commutated Thyristors (IGCTs). Large Band-Gap Materials for Devices. Power Integrated Circuits (PICs). 2. AC Machines for Drives. Induction Machines. Synchronous Machines. Variable Reluctance Machine (VRM). 3. Diodes and Phase-Controlled Converters. Diode Rectifiers. Thyristor Converters. Converter Control. EMI and Line Power Quality Problems. 4. Cycloconverters. Phase-Controlled Cycloconverters. Matrix Converters. High-Frequency Cycloconverters. 5. Voltage-Fed Converters. Single-Phase Inverters. Three-Phase Bridge Inverters. Multi-Stepped Inverters. Pulse Width Modulation Techniques. Three-Level Inverters. Hard Switching Effects. Resonant Inverters. Soft-Switched Inverters. Dynamic and Regenerative Drive Braking. PWM Rectifiers. Static VAR Compensators and Active Harmonic Filters. Introduction to Simulation-MATLAB/SIMULINK. 6. Current-Fed Converters. General Operation of a Six-Step Thyristor Inverter. Load-Commutated Inverters. Force-Commutated Inverters. Harmonic Heating and Torque Pulsation. Multi-Stepped Inverters. Inverters with Self-Commutated Devices. Current-Fed vs Voltage-Fed Converters. 7. Induction Motor Slip-Power Recovery Drives. Doubly-Fed Machine Speed Control by Rotor Rheostat. Static Kramer Drive. Static Scherius Drive. 8. Control and Estimation of Induction Motor Drives. Induction Motor Control with Small Signal Model. Scalar Control. Vector or Field-Oriented Control. Sensorless Vector Control. Direct Torque and Flux Control (DTC). Adaptive Control. Self-Commissioning of Drive. 9. Control and Estimation of Synchronous Motor Drives. Sinusoidal SPM Machine Drives. Synchronous Reluctance Machine Drives. Sinusoidal IPM Machine Drives. Trapezoidal SPM Machine Drives. Wound-Field Synchronous Machine Drives. Sensorless Control. Switched Reluctance Motor (SRM) Drives. 10. Expert System Principles and Applications. Expert System Principles. Expert System Shell. Design Methodology. Applications. Glossary. 11. Fuzzy Logic Principles and Applications. Fuzzy Sets. Fuzzy System. Fuzzy Control. General Design Methodology. Applications. Fuzzy Logic Toolbox. Glossary. 12. Neural Network Principles and Applications. The Structure of a Neuron. Artificial Neural Network. Other Networks. Neural Network in Identification and Control. General Design Methodology. Applications. Neuro-Fuzzy Systems. Demo Program with Neural Network Toolbox. Glossary. Index.
2,836 citations
"Fuzzy logic controller performance ..." refers methods in this paper
...Indirect field oriented control technique [1] allows the control the machine similar to DC machine....
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08 Oct 2000
TL;DR: In this paper, a fuzzy logic speed controller is employed in the outer loop of an IM drive for speed control of an induction motor using indirect vector control, and the performance of the proposed FLC based IM drive is compared to those obtained from the conventional proportional integral (PI) controller based drive both theoretically and experimentally at different dynamic operating conditions such as sudden change in command speed, step change in load, etc.
Abstract: This paper presents a novel speed control scheme of an induction motor (IM) using fuzzy logic control. The fuzzy logic controller (FLC) is based on the indirect vector control. The fuzzy logic speed controller is employed in the outer loop. The complete vector control scheme of the IM drive incorporating the FLC is experimentally implemented using a digital signal processor board DS-1102 for the laboratory 1 hp squirrel cage induction motor. The performances of the proposed FLC based IM drive are investigated and compared to those obtained from the conventional proportional integral (PI) controller based drive both theoretically and experimentally at different dynamic operating conditions such as sudden change in command speed, step change in load, etc. The comparative experimental results show that the FLC is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.
272 citations
"Fuzzy logic controller performance ..." refers methods in this paper
...Different schemes of Fuzzy Logic Controllers has been applied on vector control of induction machines [8][9]....
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TL;DR: A new approach to indirect vector control of induction motors by means of an expert system based on Takagi-Sugeno fuzzy reasoning, which embodies the advantages that both nonlinear controllers offer: sliding-mode controllers increasing system stability limits, and PI-like fuzzy logic based controllers reducing the chattering in permanent state.
Abstract: This paper presents a new approach to indirect vector control of induction motors. Two nonlinear controllers, one of sliding mode type and the other PI-fuzzy logic-based, define a new control structure. Both controllers are combined by means of an expert system based on Takagi-Sugeno fuzzy reasoning. The sliding-mode controller acts mainly in a transient state while the PI-like fuzzy controller acts in the steady state. The new structure embodies the advantages that both nonlinear controllers offer: sliding-mode controllers increasing system stability limits, and PI-like fuzzy logic based controllers reducing the chattering in permanent state. The scheme has been implemented and experimentally validated.
151 citations
TL;DR: In this paper, a comparison between four different speed controller design strategies based on artificial intelligence techniques is presented; two are based on tuning of conventional PI controllers, the third makes use of a fuzzy logic controller and the last is based on hybrid fuzzy sliding mode control theory.
98 citations
"Fuzzy logic controller performance ..." refers background in this paper
...It gives satisfactory performance for major of the applications [2][3]....
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