**Applied Intelligent Control of Induction Motor Drives 1st Edition By Tze Fun Chan and Keli Shi.**

Contents:

1 Introduction

2 Philosophy of Induction Motor Control

3 Modeling and Simulation of Induction Motor

4 Fundamentals of Intelligent Control Simulation

5 Expert-System-based Acceleration Control

6 Hybrid Fuzzy/PI Two-Stage Control

7 Neural-Network-based Direct Self Control

8 Parameter Estimation Using Neural Networks

9 GA-Optimized Extended Kalman Filter for Speed Estimation

10 Optimized Random PWM Strategies Based On Genetic Algorithms

11 Experimental Investigations

12 Conclusions and Future Developments

Preface: Induction motors are the most important workhorses in industry and they are manufactured in large numbers. About half of the electrical energy generated in a developed country is ultimately consumed by electric motors, of which over 90 % are induction motors. For a relatively long period, induction motors have mainly been deployed in constant-speed motor drives for general purpose applications. The rapid development of power electronic devices and converter technologies in the past few decades, however, has made possible efficient speed control by varying the supply frequency, giving rise to various forms of adjustable-speed induction motor drives. In about the same period, there were also advances in control methods and artificial intelligence (AI) techniques, including expert system, fuzzy logic, neural networks and genetic algorithm. Researchers soon realized that the performance of induction motor drives can be enhanced by adopting artificial-intelligence-based methods. Since the 1990s, AI-based induction motor drives have received greater attention and numerous technical papers have been published. Speed-sensorless induction drives have also emerged as an important branch of induction motor research. A few good reference books on intelligent control and power electronic drives were written. Some electric drive manufacturers began to incorporate AI-control in their commercial products.

This book aims to explore possible areas of induction motor control that require further investigation and development and focuses on the application of intelligent control principles and algorithms in order to make the controller independent of, or less sensitive to, motor parameter changes. Intelligent control is becoming an important and necessary method to solve difficult problems in control of induction motor drives.

Based on classical electrical machine and control theory, the authors have investigated the applications of expert-system control, fuzzy-logic control, neural-network control, and genetic algorithm to various forms of induction motor drive. This book is the result of over fifteen years of research on intelligent control of induction motors undertaken by the authors at the Department of Electrical Engineering, the Hong Kong Polytechnic University and the United States. The methods are original and most of the work has been published in IEEE Transactions and international conferences. In the past few years, our publications have been increasingly cited by Science Citation Index journal papers, showing that our work is being rigorously followed up by the induction motor drives research community. We believe that the publication of a book or monograph summarizing our latest research findings on intelligent control will benefit the research community.

This book will complement some of the fine references written by eminent electric drives and power electronic experts (such as Peter Vas, Bimal Bose, and Dote and Hoft, to name just a few), and at the same time the presentation will enable researchers to explore new research directions. Numerous examples, block diagrams, and simulation programs are provided for interested readers to conduct related investigations.

This book adopts a practical simulation approach that enables interested readers to embark on research in intelligent control of electric drives with the minimum effort and time. Intelligent control techniques have to be used in practical applications where controller designs involve noise distribution (Kalman filter), pseudo-random data (random PWM), inference similar to human, system identification, and lookup table identification. Artificial intelligence techniques are presented in the context of the drive applications being considered and a strong link between AI and the induction motor drive is established throughout the chapters. The numerous simulation examples and results presented will shed new light on possible future induction motor drives research.

There are twelve chapters in this book. Chapter 1 gives an overview of induction motor drives and reviews previous work in this important technical area. Chapter 2 presents the philosophy of induction motor control. From the classical induction motor model, the differential equations are formulated that fit in a generic control framework. Various control schemes are then discussed, followed by the development of general control algorithms. Modeling and simulation of induction motors are discussed in Chapter 3 with the aid of detailed MATLAB /Simulink block diagrams.

Chapter 4 is a primer for simulation of intelligent control systems using MATLAB / Simulink. Programming examples of fuzzy-logic, neural network, Kalman filter, and genetic algorithm are provided to familiarize readers with simulation programming involving intelligent techniques. The exercises will fast guide them into the intelligent control area. These models and simulation techniques form the basis of the intelligent control applications discussed in Chapters 5–10 which cover, in this order, expert-system-based acceleration control, hybrid fuzzy/PI two-stage control, neural-network-based direct self control, parameter estimation using neural networks, GA-optimized extended Kalman filter for speed estimation, and optimized random PWM strategy based on genetic algorithms.

In Chapter 5, an expert-system-based acceleration controller is developed to overcome the three drawbacks (sensitivity to parameter variations, error accumulation, and the needs for continuous control with initial state) of the vector controller. In every time interval of the control process, the acceleration increments produced by two different voltage vectors are compared, yielding one optimum stator voltage vector which is selected and retained. The online inference control is built using an expert system with heuristic knowledge about the relationship between the motor voltage and acceleration. Because integral calculation and motor parameters are not involved, the new controller has no accumulation error of integral as in the conventional vector control schemes and the same controller can be used for different induction motors without modification. Simulation results obtained on the expert-systembased controller show that the performance is comparable with that of a conventional direct self controller, hence proving the feasibility of expert-system-based control.

In Chapter 6, a hybrid fuzzy/PI two-stage control method is developed to optimize the dynamic performance of a current and slip frequency controller. Based on two features (current

magnitude feature and slip frequency feature) of the field orientation principle, the authors apply different strategies to control the rotor speed during the acceleration stage and the steady state stage. The performance of the two-stage controller approximates that of a field-oriented controller. Besides, the new controller has the advantages of simplicity and insensitivity to motor parameter changes. Very encouraging results are obtained from a computer simulation using MATLAB /Simulink software and a DSP-based experiment.

In Chapter 7, implementation of direct self control for an induction motor drive using artificial neural network (ANN) is discussed. ANN has the advantages of parallel computation and simple hardware, hence it is superior to a DSP-based controller in execution time and structure. In order to improve the performance of a direct self controller, an ANN-based DSC with seven layers of neurons is proposed at algorithm level. The execution time is decreased from 250 ms (for a DSP-based controller) to 21 ms (for the ANN-based controller), hence the torque and flux errors caused by long execution times are almost eliminated. A detailed simulation study is performed using MATLAB /Simulink and Neural-network Toolbox.

Machine parameter estimation is important for field-oriented control (FOC) and sensorless control. Most parameter estimation methods are based on differential equations of the induction motor. Differential operators, however, will cause noise and greatly reduce the estimation precision. Nondifferentiable points will also exist in the motor currents due to rapid turn-on or turn-off of the ideal power electronic switches. Chapter 8 addresses the issue of parameter uncertainties of induction motors and presents a neural-network-based parameter estimation method using an integral model. By using the proposed ANN-based integral models, almost all the machine parameters can be derived directly from the measured data, namely the stator currents, stator voltages and rotor speed. With the estimated parameters, load, stator flux, and rotor speed may be estimated.

Addressing the current research trend, a speed-sensorless controller using an extended Kalman filter (EKF) is investigated in Chapter 9. To improve the performance of the speedsensorless controller, noise covariance and weight matrices of the EKF are optimized by using a real-coded genetic algorithm (GA). MATLAB /Simulink based simulation and DSP-based experimental results are presented to confirm the efficacy of the GA-optimized EKF for speed estimation in an induction motor drive.

Chapter 10 is devoted to optimized random pulse-width modulation (PWM) strategies. The optimized PWM inverter can spread harmonic energy and reduce total harmonic distortion, weighted total harmonic distortion, or distortion factor. Without incurring extra hardware cost and programming complexity, the optimized PWM is implemented by writing an optimized carrier sequence into the PWM controller in place of the conventional carrier generator. Comparison between simulation and experimental results verifies that output voltage of the optimized PWM technique is superior to that based on the standard triangular PWM and random PWM methods. A real-valued genetic algorithm is employed for implementing the optimization strategy.

Chapter 11 describes the details of the experimental system and presents the experiments and experimental results. At the hardware level, an experimental system for the intelligent control of induction motor drive is proposed. The system is configured by a DSP (ADMC331), a power module (IRPT1058A), a three-phase Hall-effect current sensor, an encoder (Model GBZ02), a data acquisition card (PCL818HG), a PC host and a data-acquisition PC, as well as a 147 W three-phase induction motor. With the experimental hardware, the MATLAB /Simulink models, hybrid fuzzy/PI two-stage control algorithm, and GA-EKF method proposed in this book have been verified. It is proposed to use DSP TMS320F28335 for intelligent control with a real time data exchange (RTDX) technique. Many intelligent algorithms are complex and with larger data block (such as GA and Neural Network) which cannot be written into a DSP chip.

With the RTDX technique, hardware-in-the-loop training and simulation may be implemented in the laboratory environment. The RTDX examples of DSP target C programming and PC host MATLAB programming are provided.

Chapter 12 gives some conclusions and explores possible new developments of AI applications to induction motor drives.

This book will be useful to academics and students (senior undergraduate, postgraduate, and PhD) who specialize in electric motor drives in general and induction motor drives in particular. The readers are assumed to have a good foundation on electrical machines (including reference frame theory and transformation techniques), control theory, and basics of artificial intelligence (such as expert systems, fuzzy logic theory, neural networks, and genetic algorithms). The book is at an advanced level, but senior undergraduate students specializing on electric motor drives projects should also find it a good reference. It also provides a practical guide to research students to get started with hardware implementation of intelligent control of induction motor drives.

Tze-Fun Chan and Keli Shi

March 2010

Book Details:

⏩Edition: 1st

⏩Authors: Tze Fun Chan and Keli Shi

⏩Puplisher: Wiley-IEEE Press

⏩Puplication Date: March 15, 2011

⏩Language: English

⏩Pages: 432

⏩Size: 11.3 MB

⏩Format: PDF

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