Applied Intelligent Control of Induction Motor Drives (Chan/Applied Intelligent Control of Induction Motor Drives) ||

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. 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 transportation, will also find this book helpful. Simulation materials available for download at (http://www.wiley.com/go/chanmotor) www.wiley.com/go/chanmotor Content: Chapter 1 Introduction (pages 1–7): Chapter 2 Philosophy of Induction Motor Control (pages 9–30): Chapter 3 Modeling and Simulation of Induction Motor (pages 31–74): Chapter 4 Fundamentals of Intelligent Control Simulation (pages 75–108): Chapter 5 Expert?System?Based Acceleration Control (pages 109–132): Chapter 6 Hybrid Fuzzy/PI Two?Stage Control (pages 133–166): Chapter 7 Neural?Network?based Direct Self Control (pages 167–197): Chapter 8 Parameter Estimation Using Neural Networks (pages 199–241): Chapter 9 GA?Optimized Extended Kalman Filter for Speed Estimation (pages 243–271): Chapter 10 Optimized Random PWM Strategies Based On Genetic Algorithms (pages 273–311): Chapter 11 Experimental Investigations (pages 313–371): Chapter 12 Conclusions and Future Developments (pages 373–379): 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. * 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 transportation, will also find this book helpful. Simulation materials available for download at [www.wiley.com/go/chanmotor](http://www.wiley.com/go/chanmotor)Content: Chapter 1 Introduction (pages 1–7): Chapter 2 Philosophy of Induction Motor Control (pages 9–30): Chapter 3 Modeling and Simulation of Induction Motor (pages 31–74): Chapter 4 Fundamentals of Intelligent Control Simulation (pages 75–108): Chapter 5 Expert?System?Based Acceleration Control (pages 109–132): Chapter 6 Hybrid Fuzzy/PI Two?Stage Control (pages 133–166): Chapter 7 Neural?Network?based Direct Self Control (pages 167–197): Chapter 8 Parameter Estimation Using Neural Networks (pages 199–241): Chapter 9 GA?Optimized Extended Kalman Filter for Speed Estimation (pages 243–271): Chapter 10 Optimized Random PWM Strategies Based On Genetic Algorithms (pages 273–311): Chapter 11 Experimental Investigations (pages 313–371): Chapter 12 Conclusions and Future Developments (pages 373–379):

Since the 1990s, AI-based induction motors have received greater attention and numerous technical papers have been published. At the same time a few good reference books on intelligent control and power electronic drives were published 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. There are twelve chapters, 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. Modelling and simulation of induction motors is discussed in Chapter 3 with the aid of detailed Matlab/Simulink block diagrams. Chapter 4 is a primer for readers to get started with simulation of various AI techniques. Chapters 3 and 4 together form the basis of the intelligent control schemes discussed in Chapters 5 to 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 GA-optimized random PWM generation strategy. Chapter 11 describes the details of the experimental system and presents the experiments and experimental results. Chapter 12 gives some conclusions and explores possible new areas of AI applications to induction motor drives.