Applied intelligent control of induction motor drives / Tze-Fun Chan, Keli Shi.

Introduction -- Philosophy of Induction Motor Control -- Modeling and Simulation of Induction Motor -- Fundamentals of Intelligent Control Simulation -- 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 -- Optimized Random PWM Strategies Based On Genetic Algorithms -- Experimental Investigations -- Conclusions and Future Developments -- Appendix A: Equivalent Circuits of an Induction Motor -- Appendix B: Parameters of Induction Motors -- Appendix C: M-File of Discrete-State Induction Motor Model -- Appendix D: Expert-system Acceleration Control Algorithm -- Appendix E: Activation Functions of Neural Network -- Appendix F: M-File of Extended Kalman Filter -- Appendix G: ADMC331-based Experimental System -- Appendix H: Experiment 1: Measuring the Electrical Parameters of Motor 3 -- Appendix I: DSP Source Code for the Main Program of Experiment 2 -- Appendix J: DSP Source Code for the Main Program of Experiment 3 -- Index.

Includes bibliographical references and index.

"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 estimation. 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."--Publisher's description.

Print version record and online resource; title from PDF title page (IEEE Xplore, viewed March 2014).