Applied Intelligent Control of Induction Motor Drives

Cover Image Copyright Year: 2011
Author(s): Tze-Fun Chan; Keli Shi
Book Type: Wiley-IEEE Press
Content Type : Books & eBooks
Topics: Communication, Networking & Broadcasting ;  Components, Circuits, Devices & Systems ;  Computing & Processing ;  Power, Energy, & Industry Applications ;  Robotics & Control Systems ;  Signal Processing & Analysis ;  Transportation
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Abstract

Induction motors are the most important workhorses in industry. They are mostly used as constant-speed drives when fed from a voltage source of fixed frequency. Advent of advanced power electronic converters and powerful digital signal processors, however, has made possible the development of high performance, adjustable speed AC motor drives.

This book aims to explore new areas of induction motor control based on artificial intelligence (AI) techniques in order to make the controller less sensitive to parameter changes. Selected AI techniques are applied for different induction motor control strategies. The book presents a practical computer simulation model of the induction motor that could be used for studying various induction motor drive operations. The control strategies explored include expert-system-based acceleration control, hybrid-fuzzy/PI two-stage control, neural-network-based direct self control, and genetic algorithm based extended Kalman filter for rotor speed estima ion. There are also chapters on neural-network-based parameter estimation, genetic-algorithm-based optimized random PWM strategy, and experimental investigations. A chapter is provided as a primer for readers to get started with simulation studies on various AI techniques.

  • Presents major artificial intelligence techniques to induction motor drives
  • Uses a practical simulation approach to get interested readers started on drive development
  • Authored by experienced scientists with over 20 years of experience in the field
  • Provides numerous examples and the latest research results
  • Simulation programs available from the book's Companion Website

This book will be invaluable to graduate students and research engineers who specialize in electric motor drives, electric vehicles, and electric ship propulsion. Graduate students in intelligent control, applied electric motion, and energy, as well as engineers in industrial electronics, automation, and electrical transport tion, will also find this book helpful.

Simulation materials available for download at
www.wiley.com/go/chanmotor

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      Frontmatter

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      The prelims comprise:

    • Half-Title Page

    • Title Page

    • Copyright Page

    • Table of Contents

    • Preface

    • Acknowledgments

    • About the Authors

    • List of Symbols

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    Introduction

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    A historical account of induction motor control is given, including scalar control, vector control, speed sensorless control and intelligent control. The contents of the book are briefly reviewed.

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    Philosophy of Induction Motor Control

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    Induction motor control is explained from the perspectives of motor theory and control algorithms. The hardware requirements of a modern induction motor drive are briefly mentioned. A taxonomy of induction motor control is given.

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    Modeling and Simulation of Induction Motor

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    Transient models of a three-phase induction motor are established. Functional blocks of the entire inverter-fed induction motor drive system are developed systematically. Programming examples are given to familiarize readers with MATLAB¿¿/Simulink programming environment.

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    Fundamentals of Intelligent Control Simulation

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    This chapter presents the fundamentals of intelligent control simulation using MATLAB¿¿/Simulink. Modeling and simulation of a fuzzy logic based PI controller, neural network based Park's transformation, signal measurement using Kalman filter, and a genetic algorithm optimized PID controller are discussed.

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    Expert-System-Based Acceleration Control

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    An acceleration control scheme for an induction motor drive is developed using an expert system approach. The acceleration increments produced by two different voltage vectors are compared to give an optimum stator voltage vector which is selected and retained. The on-line inference control is built using an expert system with heuristic knowledge about the relationship between the motor voltage and acceleration. The feasibility of the proposed system is validated by simulation studies.

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    Hybrid Fuzzy/PI Two-Stage Control

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    Based on the two-stage strategy and the heuristics deduced from the field-oriented principle, a hybrid fuzzy/PI controller is proposed in this chapter. A fuzzy logic controller provides frequency control during the acceleration/deceleration stage, giving a large torque. A PI controller provides speed control by regulating the current magnitude during 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.

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    Neural-Network-based Direct Self Control

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    In this chapter, implementation of direct self control for an induction motor drive using artificial neural network (ANN) is discussed. The computationally intensive direct self control is implemented using ANNs. Fixed weight and supervised networks with individual training strategy are employed for the ANN controller design. Neural-network based DSC is fast, giving very small steady-state control error.

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    Parameter Estimation Using Neural Networks

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    This chapter describes an integral model for an induction motor which can be implemented conveniently using artificial neural networks. The coefficients of the integral equations are obtained by proper training of the neural networks using actual motor test data and a realistic simulation model of the induction motor can be constructed.

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    GA-Optimized Extended Kalman Filter for Speed Estimation

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    In this chapter, a speed-sensorless controller using an extended Kalman filter (EKF) is investigated. To improve the performance of the speed-sensorless controller, noise covariance and weight matrices of the EKF are optimized by using a real-coded genetic algorithm (GA). MATLAB¿¿/Simulink based simulation results are presented to confirm the efficacy of the GA-optimized EKF for speed estimation in an induction motor drive.

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    Optimized Random PWM Strategies Based On Genetic Algorithms

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    In this chapter, real-coded genetic algorithm is employed to optimize a single-phase inverter controlled by random PWM methods. The simulation studies show that the GA-optimized random PWM is superior to conventional PWM techniques when harmonic energy spread and THD are considered. DSP-based inverter experiments confirm the feasibility of implementation of GA-optimized random-carrier-frequency PWM.

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    Experimental Investigations

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    This chapter discusses how a low-cost functional experimental drive system with data acquisition capability is implemented using ADMC331 DSP. The system enables experimental studies on motor run up, fuzzy/PI control, and GA-EKF speed estimation to be performed. Modern DSP programming techniques for induction motor control are presented with reference to the TMS320F28335 processor. These techniques enable the necessary signals to be acquired and transferred to the host PC in real-time for use by a complex control algorithm.

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    Conclusions and Future Developments

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    This chapter summarizes the contributions of the book on intelligent control of induction motor drives. Potential industrial applications of these intelligent induction motor drives are discussed. A few new research topics on intelligent induction motor control are reviewed.

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    Appendix A: Equivalent Circuits of an Induction Motor

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    Appendix B: Parameters of Induction Motors

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    Appendix C: M-File of Discrete-State Induction Motor Model

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    Appendix D: Expert-system Acceleration Control Algorithm

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    Appendix E: Activation Functions of Neural Network

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    Appendix F: M-File of Extended Kalman Filter

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    Appendix G: ADMC331-Based Experimental System

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    Appendix H: Experiment 1: Measuring the Electrical Parameters of Motor 3

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    Appendix I: DSP Source Code for the Main Program of Experiment 2

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    Appendix J: DSP Source Code for the Main Program of Experiment 3

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    Index

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