Applied Intelligent Control of Induction

Since the 1990s, AIbased 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, expertsystembased acceleration control, hybrid fuzzy/PI twostage control, neuralnetworkbased direct self control, parameter estimation using neural networks, GAoptimized extended Kalman filter for speed estimation, and GAoptimized 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.

Tze-Fun Chan is an associate professor of electrical engineering at the Hong Kong Polytechnic University, where he has been working for over 30 years since 1978. Chan’s research interests are self-excited induction generators, brushless AC generators, permanent-magnet machines, finite element analysis of electric machines, and electric motor drives control. In June 2006, Chan was awarded a Prize Paper by IEEE Power Engineering Society Power Generation and Energy Development Committee. In 2007, Chan co-authored a book published by Wiley. He received the B.Sc. and M.Phil degrees in electrical engineering from the University of Hong Kong in 1974 and 1980 respectively. He received his PhD degree in electrical engineering from City University London in 2005.

Keli Shi is a Research Engineer of Netpower Technologies Inc.. His research interests are DSP applications and intelligent control of induction and permanent magnet machines. He received his BS degree in electronics and electrical engineering from Chengdu University of Science and Technology and MS degree in electrical engineering from Harbin Institute of Technology in 1983 and 1989 respectively. He received his PhD in electrical engineering from The Hong Kong Polytechnic University in 2001.

Preface xiii

Acknowledgments xvii

About the Authors xxi

List of Symbols xxiii

1 Introduction 1

1.1 Induction Motor 1

1.2 Induction Motor Control 2

1.3 Review of Previous Work 2

1.3.1 Scalar Control 3

1.3.2 Vector Control 3

1.3.3 Speed Sensorless Control 4

1.3.4 Intelligent Control of Induction Motor 4

1.3.5 Application Status and Research Trends of Induction Motor Control 4

1.4 Present Study 4

2 Philosophy of Induction Motor Control 9

2.1 Introduction 9

2.2 Induction Motor Control Theory 10

2.2.1 Nonlinear Feedback Control 10

2.2.2 Induction Motor Models 11

2.2.3 Field-Oriented Control 13

2.2.4 Direct Self Control 14

2.2.5 Acceleration Control Proposed 15

2.2.6 Need for Intelligent Control 16

2.2.7 Intelligent Induction Motor Control Schemes 17

2.3 Induction Motor Control Algorithms 19

2.4 Speed Estimation Algorithms 23

2.5 Hardware 25

3 Modeling and Simulation of Induction Motor 31

3.1 Introduction 31

3.2 Modeling of Induction Motor 32

3.3 Current-Input Model of Induction Motor 34

3.3.1 Current (3/2) Rotating Transformation Sub-Model 35

3.3.2 Electrical Sub-Model 35

3.3.3 Mechanical Sub-Model 37

3.3.4 Simulation of Current-Input Model of Induction Motor 37

3.4 Voltage-Input Model of Induction Motor 40

3.4.1 Simulation Results of ‘Motor 1’ 43

3.4.2 Simulation Results of ‘Motor 2’ 43

3.4.3 Simulation Results of ‘Motor 3’ 44

3.5 Discrete-State Model of Induction Motor 45

3.6 Modeling and Simulation of Sinusoidal PWM 49

3.7 Modeling and Simulation of Encoder 51

3.8 Modeling of Decoder 54

3.9 Simulation of Induction Motor with PWM Inverter and Encoder/Decoder 54

3.10 MATLAB/Simulink Programming Examples 55

3.11 Summary 73

4 Fundamentals of Intelligent Control Simulation 75

4.1 Introduction 75

4.2 Getting Started with Fuzzy Logical Simulation 75

4.2.1 Fuzzy Logic Control 75

4.2.2 Example: Fuzzy PI Controller 77

4.3 Getting Started with Neural-Network Simulation 83

4.3.1 Artificial Neural Network 83

4.3.2 Example: Implementing Park’s Transformation Using ANN 85

4.4 Getting Started with Kalman Filter Simulation 90

4.4.1 Kalman Filter 92

4.4.2 Example: Signal Estimation in the Presence of Noise by Kalman Filter 94

4.5 Getting Started with Genetic Algorithm Simulation 98

4.5.1 Genetic Algorithm 98

4.5.2 Example: Optimizing a Simulink Model by Genetic Algorithm 100

4.6 Summary 107

5 Expert-System-based Acceleration Control 109

5.1 Introduction 109

5.2 Relationship between the Stator Voltage Vector and Rotor Acceleration 110

5.3 Analysis of Motor Acceleration of the Rotor 113

5.4 Control Strategy of Voltage Vector Comparison and Voltage Vector Retaining 114

5.5 Expert-System Control for Induction Motor 118

5.6 Computer Simulation and Comparison 122

5.6.1 The First Simulation Example 123

5.6.2 The Second Simulation Example 125

5.6.3 The Third Simulation Example 126

5.6.4 The Fourth Simulation Example 127

5.6.5 The Fifth Simulation Example 129

5.7 Summary 131

6 Hybrid Fuzzy/PI Two-Stage Control 133

6.1 Introduction 133

6.2 Two-Stage Control Strategy for an Induction Motor 135

6.3 Fuzzy Frequency Control 136

6.3.1 Fuzzy Database 138

6.3.2 Fuzzy Rulebase 139

6.3.3 Fuzzy Inference 141

6.3.4 Defuzzification 142

6.3.5 Fuzzy Frequency Controller 142

6.4 Current Magnitude PI Control 143

6.5 Hybrid Fuzzy/PI Two-Stage Controller for an Induction Motor 145

6.6 Simulation Study on a 7.5 kW Induction Motor 145

6.6.1 Comparison with Field-Oriented Control 146

6.6.2 Effects of Parameter Variation 148

6.6.3 Effects of Noise in the Measured Speed and Input Current 149

6.6.4 Effects of Magnetic Saturation 149

6.6.5 Effects of Load Torque Variation 150

6.7 Simulation Study on a 0.147 kW Induction Motor 152

6.8 MATLAB/Simulink Programming Examples 158

6.8.1 Programming Example 1: Voltage-Input Model of an Induction Motor 158

6.8.2 Programming Example 2: Fuzzy/PI Two-Stage Controller 163

6.9 Summary 165

7 Neural-Network-based Direct Self Control 167

7.1 Introduction 167

7.2 Neural Networks 168

7.3 Neural-Network Controller of DSC 170

7.3.1 Flux Estimation Sub-Net 170

7.3.2 Torque Calculation Sub-Net 171

7.3.3 Flux Angle Encoder and Flux Magnitude Calculation Sub-Net 173

7.3.4 Hysteresis Comparator Sub-Net 178

7.3.5 Optimum Switching Table Sub-Net 180

7.3.6 Linking of Neural Networks 183

7.4 Simulation of Neural-Network-based DSC 184

7.5 MATLAB/Simulink Programming Examples 187

7.5.1 Programming Example 1: Direct Self Controller 187

7.5.2 Programming Example 2: Neural-Network-based Optimum Switching Table 192

7.6 Summary 196

8 Parameter Estimation Using Neural Networks 199

8.1 Introduction 199

8.2 Integral Equations Based on the ‘T’ Equivalent Circuit 200

8.3 Integral Equations based on the ‘G’ Equivalent Circuit 203

8.4 Parameter Estimation of Induction Motor Using ANN 205

8.4.1 Estimation of Electrical Parameters 206

8.4.2 ANN-based Mechanical Model 208

8.4.3 Simulation Studies 210

8.5 ANN-based Induction Motor Models 214

8.6 Effect of Noise in Training Data on Estimated Parameters 217

8.7 Estimation of Load, Flux and Speed 218

8.7.1 Estimation of Load 218

8.7.2 Estimation of Stator Flux 222

8.7.3 Estimation of Rotor Speed 226

8.8 MATLAB/Simulink Programming Examples 231

8.8.1 Programming Example 1: Field-Oriented Control (FOC) System 231

8.8.2 Programming Example 2: Sensorless Control of Induction Motor 234

8.9 Summary 240

9 GA-Optimized Extended Kalman Filter for Speed Estimation 243

9.1 Introduction 243

9.2 Extended State Model of Induction Motor 244

9.3 Extended Kalman Filter Algorithm for Rotor Speed Estimation 245

9.3.1 Prediction of State 245

9.3.2 Estimation of Error Covariance Matrix 245

9.3.3 Computation of Kalman Filter Gain 245

9.3.4 State Estimation 246

9.3.5 Update of the Error Covariance Matrix 246

9.4 Optimized Extended Kalman Filter 247

9.5 Optimizing the Noise Matrices of EKF Using GA 250

9.6 Speed Estimation for a Sensorless Direct Self Controller 253

9.7 Speed Estimation for a Field-Oriented Controller 255

9.8 MATLAB/Simulink Programming Examples 260

9.8.1 Programming Example 1: Voltage-Frequency Controlled (VFC) Drive 260

9.8.2 Programming Example 2: GA-Optimized EKF for Speed Estimation 264

9.8.3 Programming Example 3: GA-based EKF Sensorless Voltage-Frequency Controlled Drive 268

9.8.4 Programming Example 4: GA-based EKF Sensorless FOC Induction Motor Drive 269

9.9 Summary 270

10 Optimized Random PWM Strategies Based On Genetic Algorithms 273

10.1 Introduction 273

10.2 PWM Performance Evaluation 274

10.2.1 Fourier Analysis of PWM Waveform 276

10.2.2 Harmonic Evaluation of Typical Waveforms 277

10.3 Random PWM Methods 283

10.3.1 Random Carrier-Frequency PWM 283

10.3.2 Random Pulse-Position PWM 285

10.3.3 Random Pulse-Width PWM 285

10.3.4 Hybrid Random Pulse-Position and Pulse-Width PWM 286

10.3.5 Harmonic Evaluation Results 287

10.4 Optimized Random PWM Based on Genetic Algorithm 288

10.4.1 GA-Optimized Random Carrier-Frequency PWM 289

10.4.2 GA-Optimized Random-Pulse-Position PWM 290

10.4.3 GA-Optimized Random-Pulse-Width PWM 292

10.4.4 GA-Optimized Hybrid Random Pulse-Position and Pulse-Width PWM 293

10.4.5 Evaluation of Various GA-Optimized Random PWM Inverters 295

10.4.6 Switching Loss of GA-Optimized Random Single-Phase PWM Inverters 296

10.4.7 Linear Modulation Range of GA-Optimized Random Single-Phase PWM Inverters 297

10.4.8 Implementation of GA-Optimized Random Single-Phase PWM Inverter 298

10.4.9 Limitations of Reference Sinusoidal Frequency of GA-Optimized Random PWM Inverters 298

10.5 MATLAB/Simulink Programming Examples 299

10.5.1 Programming Example 1: A Single-Phase Sinusoidal PWM 299

10.5.2 Programming Example 2: Evaluation of a Four-Pulse Wave 302

10.5.3 Programming Example 3: Random Carrier-Frequency

10.6 Experiments on Various PWM Strategies 305

10.6.1 Implementation of PWM Methods Using DSP 305

10.6.2 Experimental Results 307

10.7 Summary 310

11 Experimental Investigations 313

11.1 Introduction 313

11.2 Experimental Hardware Design for Induction Motor Control 314

11.2.1 Hardware Description 314

11.3 Software Development Method 320

11.4 Experiment 1: Determination of Motor Parameters 321

11.5 Experiment 2: Induction Motor Run Up 321

11.5.1 Program Design 322

11.5.2 Program Debug 324

11.5.3 Experimental Investigations 327

11.6 Experiment 3: Implementation of Fuzzy/PI Two-Stage Controller 330

11.6.1 Program Design 330

11.6.2 Program Debug 338

11.6.3 Performance Tests 339

11.7 Experiment 4: Speed Estimation Using a GA-Optimized Extended Kalman Filter 344

11.7.1 Program Design 345

11.7.2 GA-EKF Experimental Method 345

11.7.3 GA-EKF Experiments 346

11.7.4 Limitations of GA-EKF 349

11.8 DSP Programming Examples 352

11.8.1 Generation of 3-Phase Sinusoidal PWM 354

11.8.2 RTDX Programming 359

11.8.3 ADC Programming 361

11.8.4 CAP Programming 364

11.9 Summary 370

12 Conclusions and Future Developments 373

12.1 Main Contributions of the Book 374

12.2 Industrial Applications of New Induction Motor Drives 375

12.3 Future Developments 377

12.3.1 Expert-System-based Acceleration Control 378

12.3.2 Hybrid Fuzzy/PI Two-Stage Control 378

12.3.3 Neural-Network-based Direct Self Control 378

12.3.4 Genetic Algorithm for an Extended Kalman Filter 378

12.3.5 Parameter Estimation Using Neural Networks 378

12.3.6 Optimized Random PWM Strategies Based on Genetic Algorithms 378

12.3.7 AI-Integrated Algorithm and Hardware 379

Appendix A Equivalent Circuits of an Induction Motor 381

Appendix B Parameters of Induction Motors 383

Appendix C M-File of Discrete-State Induction Motor Model 385

Appendix D Expert-System Acceleration Control Algorithm 387

Appendix E Activation Functions of Neural Network 391

Appendix F M-File of Extended Kalman Filter 393

Appendix G ADMC331-based Experimental System 395

Appendix H Experiment 1: Measuring the Electrical Parameters of Motor 3 397

Appendix I DSP Source Code for the Main Program of Experiment 2 403

Appendix J DSP Source Code for the Main Program of Experiment 3 407

Index.

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Applied Intelligent Control of Induction Motor Drives

Summary

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Table of Contents

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