A novel self - Tuning fuzzy based PID controller for speed control of induction motor drive
01 Dec 2013-pp 62-67
TL;DR: A self-tuning fuzzy (STF) PID controller, Fuzzy Logic Controller and conventional PID controller based speed control system for a current source PWM inverter fed indirect field oriented control of Induction Motor (IM) Drives is presented.
Abstract: This paper presents a comparative study between a self-tuning fuzzy (STF) PID controller, Fuzzy Logic Controller and conventional PID controller based speed control system for a current source PWM inverter fed indirect field oriented control of Induction Motor (IM) Drives. In this work the conventional PI controller is replaced by self-tuning fuzzy PID based intelligent controller. The fuzzy logic controller employs different types of membership functions for each parameter for the efficient control of the drive system. The performance of the self-tuning fuzzy PID based controller is analyzed using digital simulation in MATLAB/Simulink. The results are compared with conventional PID and Fuzzy Logic Controller. The self-tuning fuzzy logic controller gives better results.
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TL;DR: A novel technique for the fast tuning of the parameters of the proportional–integral–derivative (PID) controller of a second-order heat, ventilation, and air conditioning (HVAC) system using a fast convergence evolution algorithm, called Big Bang–Big Crunch (BB–BC).
Abstract: This article presents a novel technique for the fast tuning of the parameters of the proportional–integral–derivative (PID) controller of a second-order heat, ventilation, and air conditioning (HVAC) system. The HVAC systems vary greatly in size, control functions and the amount of consumed energy. The optimal design and power efficiency of an HVAC system depend on how fast the integrated controller, e.g., PID controller, is adapted in the changes of the environmental conditions. In this paper, to achieve high tuning speed, we rely on a fast convergence evolution algorithm, called Big Bang–Big Crunch (BB–BC). The BB–BC algorithm is implemented, along with the PID controller, in an FPGA device, in order to further accelerate of the optimization process. The FPGA-in-the-loop (FIL) technique is used to connect the FPGA board (i.e., the PID and BB–BC subsystems) with the plant (i.e., MATLAB/Simulink models of HVAC) in order to emulate and evaluate the entire system. The experimental results demonstrate the efficiency of the proposed technique in terms of optimization accuracy and convergence speed compared with other optimization approaches for the tuning of the PID parameters: sw implementation of the BB–BC, genetic algorithm (GA), and particle swarm optimization (PSO).
24 citations
Cites methods from "A novel self - Tuning fuzzy based P..."
...The tuning of the parameters of the PID controllers has been proved a hard task and several approaches have been investigated in the past [1,2] using analytical methods, heuristic methods, such as Ziegler–Nichols (Z–N) rule, frequency response methods, artificial intelligence such as fuzzy logic [3], and iterative learning approaches [4]....
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TL;DR: In this paper, the authors presented various adaptive and robust control strategies, namely (a) fuzzy logic controller (FLC) based on Levenberg-Marquardt algorithm (LMA), (b) FLC based on steepest descent algorithm (SDA), (c) FLC based on Newton algorithm (NA), and (d) FLCA based on Gauss-Newton algorithm (GNA) for the indirect vector control (IVC) three-phase IM.
Abstract: Currently, in high-performance applications, the vector control (VC) scheme of induction motor (IM) is widely employed in industry. The VC scheme has significant features of decoupling torque and flux; also, its hardware implementation is easier. Conventionally, PID control schemes are frequently used for variable speed operation. However, the performance of the VC scheme is limited over a wide range of speed operation because of de-tuning caused by parameter uncertainties. To address the aforementioned challenging problem, adaptive and robust control strategies are mostly implemented. This paper presents various novel, adaptive, and robust control strategies, namely (a) fuzzy logic controller (FLC) based on Levenberg–Marquardt algorithm (LMA), (b) FLC based on steepest descent algorithm (SDA), (c) FLC based on Newton algorithm (NA), and (d) FLC based on Gauss–Newton algorithm (GNA) for the indirect vector control (IVC) three-phase IM. The focal motive is to accomplish fast dynamic response with fault-tolerant capability, load disturbance rejection qualities, insensitivity to the parameter uncertainties, robustness to speed variation, and to acquire maximum efficiency as well as torque. The $$d-q$$
modeling of the IVC IM in the synchronous reference frame and space vector pulse width modulation (SVPWM) employed in inverter are designed in MATLAB/Simulink. Our work also presents critical, analytical, and comparative assessment of the proposed controllers with traditional tuned PI control strategy for the electrical faults perturbation, load disturbances, speed variations, and parameter uncertainties. Furthermore, the simulation results of the above-mentioned designed control strategies validated robust, smooth, and faster response with permissible overshoot, undershoot, settling time, and rise time for the IVC IM drive system, compared to prior works.
21 citations
TL;DR: In this article, the PI and fuzzy PI controllers were used to reduce hazardous indoor airbenzene concentrations in small workshops in order to prevent harmful conditions for workers in indoor environments.
Abstract: Departamento de Ingenieria Electrica y Electronica, Instituto Tecnologico de Tijuana, Tijuana,Baja California C.P. 22414, Mexico; E-Mail:nohe@ieee.org* Author to whom correspondence should be addressed;E-Mail: npitalua@industrial.uson.mx;Tel.: +52-662-2592159; Fax: +52-662-2592160.Academic Editor: Marc A. RosenReceived: 17 January 2015 / Accepted: 24 April 2015 / Published: 4 May 2015Abstract: Exposure to hazardous concentrations of volatile organic compounds indoorsin small workshops could affect the health of workers, resulting in respirative diseases,severe intoxication or even cancer. Controlling the concentration of volatile organiccompounds is required to prevent harmful conditions for workers in indoor environments.In this document, PI and fuzzy PI controllers were used to reduce hazardous indoor airbenzene concentrations in small workplaces. The workshop is represented by means of awell-mixed room model. From the knowledge obtained from the model, PI and fuzzy PIcontrollers were designed and their performances were compared. Both controllers wereable to maintain the benzene concentration within secure levels for the workers. The fuzzyPI controller performed more efficiently than the PI controller. Both approaches couldbe expanded to control multiple extractor fans in order to reduce the air pollution in a
18 citations
TL;DR: The main theme is to design a robust control scheme having faster dynamic response, reliable operation for parameter uncertainties and speed variation, and maximized torque and efficiency of the IM.
Abstract: Recently, the Indirect Field Oriented Control (IFOC) scheme for Induction Motors (IM) has gained wide acceptance in high performance applications The IFOC has remarkable characteristics of decoupling torque and flux along with an easy hardware implementation However, the detuning limits the performance of drives due to uncertainties of parameters Conventionally, the use of a Proportional Integral Differential (PID) controller has been very frequent in variable speed drive applications However, it does not allow for the operation of an IM in a wide range of speeds In order to tackle these problems, optimal, robust, and adaptive control algorithms are mostly in use The work presented in this paper is based on new optimal, robust, and adaptive control strategies, including an Adaptive Proportional Integral (PI) controller, sliding mode control, Fuzzy Logic (FL) control based on Steepest Descent (SD), Levenberg-Marquardt (LM) algorithms, and Hybrid Control (HC) or adaptive sliding mode controller to overcome the deficiency of conventional control strategies The main theme is to design a robust control scheme having faster dynamic response, reliable operation for parameter uncertainties and speed variation, and maximized torque and efficiency of the IM The test bench of the IM control has three main parts: IM model, Inverter Model, and control structure The IM is modelled in synchronous frame using d q modelling while the Space Vector Pulse Width Modulation (SVPWM) technique is used for modulation of the inverter Our proposed controllers are critically analyzed and compared with the PI controller considering different conditions: parameter uncertainties, speed variation, load disturbances, and under electrical faults In addition, the results validate the effectiveness of the designed controllers and are then related to former works
16 citations
Cites background from "A novel self - Tuning fuzzy based P..."
...The transient oscillation, settling time, and SSE increase comparatively less than with parameter and load variation....
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...The HC scheme results validate the insensitivity and robustness to parameter and load variation with fast settling time and zero SSE....
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...Rotor Resistance and Load Disturbances The disturbance rejection capability for the proposed control schemes was verified by applying a ful load til t = s, and for the rest of the test the load was halved. oreover, to verify the robustness of the design co tr l str t i f Rr was varied from rated to 120%, 150%, and 200% of the rated values. s ill str t i i t rat Rr, the PI control er simulation results confir slow convergence, high transient oscil ation, and SSE for parameter and load variatio . l ti of the proposed optimal control schemes is elaborated in Table 1....
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...The proposed control design provides better and faster dynamic response with a zero Steady State Error (SSE) [22]....
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...The FLC based on the SD technique SSE increases from 0.21 rad/s to 0.34 rad/s with parameter and load variation from rated Rr to 200% Rr as illustrated in Figure 14....
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17 Mar 2014
TL;DR: In this article, the particle swarm optimization (PSO) algorithm is used to tune each parameter of PI speed controller to improve the speed response performance of the three phase induction motor.
Abstract: Optimization techniques are become more popular for the improvement in control of Three Phase Induction Motor (TIM). Also the Volt/Hz (V/f) control and space vector pulse width modulation (SVPWM) are used to reduce the harmonics level related to other control and modulation techniques. This paper deals with the tuning of PI controller parameters to be used in TIM. Particle Swarm Optimization (PSO) algorithm is used to tune each parameter of PI speed controller to improve the speed response performance of the TIM. By designing appropriate PSO algorithm, Kp and Ki of the PI speed controller parameters are tuned for TIM operation with SVPWM Switching and V/f Control. The performance of PI speed controller on the TIM is measured by estimating the change of speed and torque under speed response condition. It is found that the performance of the PI controller is robust in terms of overshoot, settling time, steady state error and RMSE.
16 citations
Cites background or methods from "A novel self - Tuning fuzzy based P..."
...The tuning methods for proportional-integral (PI) controllers are used the Ziegler–Nichols method and self-tuning fuzzy PI based intelligent controller [1, 13]....
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...It is advantage used for solves many problems such as high overshoot, high steady state error, oscillation of speed response and torque due to changes in mechanical load [1]....
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...TIMs are the most used electric drives in the industries and high performance variable speed drive application because its robustness, high efficiency reduced maintenance, high performance and low cost [1]....
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References
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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
"A novel self - Tuning fuzzy based P..." refers methods in this paper
...The use of PID controller induces many problems like high overshoot, oscillation of speed and torque due to sudden changes in load and external disturbances [2, 11]....
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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
TL;DR: A fuzzy model reference learning control technique is applied to an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve the desired speed performance.
Abstract: Indirect field orientation (IFO) induction machine drives are increasingly employed in industrial drive systems, but the drive performance often degrades due to machine parameter variations. In this paper, a fuzzy model reference learning control technique is applied to an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve the desired speed performance. Experimental results are presented to verify the effectiveness of the proposed control scheme.
90 citations
01 Jan 2008
TL;DR: In this paper, a self-tuning fuzzy PI controller (STFPIC) is used for the supply air pressure control loop for Heating,Ventilation and Air-Conditioning (HVAC) system.
Abstract: —In this paper, a Self-tuning Fuzzy PI controlleris used for the supply air pressure control loop for Heating,Ventilation and Air-Conditioning (HVAC) system. The self-tuningFuzzy PI controller (STFPIC) is adjusted the output scalingfactor on-line by fuzzy rules according to the current trend of thecontrolled process. The rule-base for tuning the output-scalingfactor is defined on error and change of error of the controlledvariable. Ziegler-Nichols tuned PI or PID controller performswell around normal working conditions, but its tolerance toprocess parameter variations are severely affected. The STFPICis used here to overcome these shortcomings. Comparing withPID and Adaptive Neuro-Fuzzy (ANF) Controllers, simulationsresults show that STFPIC performances are better under normalconditions as well as when the HVAC system encounters largeparameter variations. Copyright ° c 2008 Yang’s Scientific ResearchInstitute, LLC. All rights reserved.Index Terms —PID control, HVAC system, self-tuning fuzzy PIcontroller, adaptive neuro-fuzzy method..
54 citations
08 Feb 2012
TL;DR: Simulation results show that, the performance of a pitch control system has improved significantly using self-tuning fuzzy PID compared to conventional PID controller.
Abstract: In this paper self-tuning fuzzy PID controller is developed to improved the performance for a pitch control of aircraft system. The controller is designed based on the dynamic modeling of system begins with a derivation of suitable mathematical model to describe the longitudinal motion of an aircraft. Fuzzy logic is used to tune each parameter of Proportional-integral-derivative (PID) controller by selecting appropriate fuzzy rules through simulation in Mat lab and Simulink. Comparative assessment based on time response specifications between conventional PID controller with self-tuning fuzzy PID for an autopilot of longitudinal dynamic in pitch aircraft is investigated and analyzed. Simulation results show that, the performance of a pitch control system has improved significantly using self-tuning fuzzy PID compared to conventional PID controller.
52 citations