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Applied intelligent control of induction motor drives / / Tze-Fun Chan, Keli Shi
Applied intelligent control of induction motor drives / / Tze-Fun Chan, Keli Shi
Autore Chan Tze Fun
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , c2011
Descrizione fisica 1 online resource (450 p.)
Disciplina 621.46
Altri autori (Persone) ShiKeli
Soggetto topico Intelligent control systems
Electric motors, Induction
ISBN 0-470-82828-5
1-299-18616-5
0-470-82557-X
0-470-82558-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910133596803321
Chan Tze Fun  
Hoboken, New Jersey : , : Wiley, , c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied intelligent control of induction motor drives / / Tze-Fun Chan, Keli Shi
Applied intelligent control of induction motor drives / / Tze-Fun Chan, Keli Shi
Autore Chan Tze Fun
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , c2011
Descrizione fisica 1 online resource (450 p.)
Disciplina 621.46
Altri autori (Persone) ShiKeli
Soggetto topico Intelligent control systems
Electric motors, Induction
ISBN 0-470-82828-5
1-299-18616-5
0-470-82557-X
0-470-82558-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910676546403321
Chan Tze Fun  
Hoboken, New Jersey : , : Wiley, , c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied intelligent control of induction motor drives / / Tze-Fun Chan, Keli Shi
Applied intelligent control of induction motor drives / / Tze-Fun Chan, Keli Shi
Autore Chan Tze Fun
Pubbl/distr/stampa Hoboken, N.J., : IEEE Press, c2011
Descrizione fisica 1 online resource (450 p.)
Disciplina 621.46
Altri autori (Persone) ShiKeli
Soggetto topico Intelligent control systems
Electric motors, Induction
ISBN 0-470-82828-5
1-299-18616-5
0-470-82557-X
0-470-82558-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910876855003321
Chan Tze Fun  
Hoboken, N.J., : IEEE Press, c2011
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