{"product_id":"applied-intelligent-control-of-9780470825563","title":"Applied Intelligent Control of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eInduction 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii  \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003eAbout the Authors xxi\u003c\/p\u003e \u003cp\u003eList of Symbols xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Induction Motor 1\u003c\/p\u003e \u003cp\u003e1.2 Induction Motor Control 2\u003c\/p\u003e \u003cp\u003e1.3 Review of Previous Work 2\u003c\/p\u003e \u003cp\u003e1.3.1 Scalar Control 3\u003c\/p\u003e \u003cp\u003e1.3.2 Vector Control 3\u003c\/p\u003e \u003cp\u003e1.3.3 Speed Sensorless Control 4\u003c\/p\u003e \u003cp\u003e1.3.4 Intelligent Control of Induction Motor 4\u003c\/p\u003e \u003cp\u003e1.3.5 Application Status and Research Trends of Induction Motor Control 4\u003c\/p\u003e \u003cp\u003e1.4 Present Study 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Philosophy of Induction Motor Control 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.2 Induction Motor Control Theory 10\u003c\/p\u003e \u003cp\u003e2.2.1 Nonlinear Feedback Control 10\u003c\/p\u003e \u003cp\u003e2.2.2 Induction Motor Models 11\u003c\/p\u003e \u003cp\u003e2.2.3 Field-Oriented Control 13\u003c\/p\u003e \u003cp\u003e2.2.4 Direct Self Control 14\u003c\/p\u003e \u003cp\u003e2.2.5 Acceleration Control Proposed 15\u003c\/p\u003e \u003cp\u003e2.2.6 Need for Intelligent Control 16\u003c\/p\u003e \u003cp\u003e2.2.7 Intelligent Induction Motor Control Schemes 17\u003c\/p\u003e \u003cp\u003e2.3 Induction Motor Control Algorithms 19\u003c\/p\u003e \u003cp\u003e2.4 Speed Estimation Algorithms 23\u003c\/p\u003e \u003cp\u003e2.5 Hardware 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Modeling and Simulation of Induction Motor 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 31\u003c\/p\u003e \u003cp\u003e3.2 Modeling of Induction Motor 32\u003c\/p\u003e \u003cp\u003e3.3 Current-Input Model of Induction Motor 34\u003c\/p\u003e \u003cp\u003e3.3.1 Current (3\/2) Rotating Transformation Sub-Model 35\u003c\/p\u003e \u003cp\u003e3.3.2 Electrical Sub-Model 35\u003c\/p\u003e \u003cp\u003e3.3.3 Mechanical Sub-Model 37\u003c\/p\u003e \u003cp\u003e3.3.4 Simulation of Current-Input Model of Induction Motor 37\u003c\/p\u003e \u003cp\u003e3.4 Voltage-Input Model of Induction Motor 40\u003c\/p\u003e \u003cp\u003e3.4.1 Simulation Results of ‘Motor 1’ 43\u003c\/p\u003e \u003cp\u003e3.4.2 Simulation Results of ‘Motor 2’ 43\u003c\/p\u003e \u003cp\u003e3.4.3 Simulation Results of ‘Motor 3’ 44\u003c\/p\u003e \u003cp\u003e3.5 Discrete-State Model of Induction Motor 45\u003c\/p\u003e \u003cp\u003e3.6 Modeling and Simulation of Sinusoidal PWM 49\u003c\/p\u003e \u003cp\u003e3.7 Modeling and Simulation of Encoder 51\u003c\/p\u003e \u003cp\u003e3.8 Modeling of Decoder 54\u003c\/p\u003e \u003cp\u003e3.9 Simulation of Induction Motor with PWM Inverter and Encoder\/Decoder 54\u003c\/p\u003e \u003cp\u003e3.10 MATLAB\/Simulink Programming Examples 55\u003c\/p\u003e \u003cp\u003e3.11 Summary 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Fundamentals of Intelligent Control Simulation 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 75\u003c\/p\u003e \u003cp\u003e4.2 Getting Started with Fuzzy Logical Simulation 75\u003c\/p\u003e \u003cp\u003e4.2.1 Fuzzy Logic Control 75\u003c\/p\u003e \u003cp\u003e4.2.2 Example: Fuzzy PI Controller 77\u003c\/p\u003e \u003cp\u003e4.3 Getting Started with Neural-Network Simulation 83\u003c\/p\u003e \u003cp\u003e4.3.1 Artificial Neural Network 83\u003c\/p\u003e \u003cp\u003e4.3.2 Example: Implementing Park’s Transformation Using ANN 85\u003c\/p\u003e \u003cp\u003e4.4 Getting Started with Kalman Filter Simulation 90\u003c\/p\u003e \u003cp\u003e4.4.1 Kalman Filter 92\u003c\/p\u003e \u003cp\u003e4.4.2 Example: Signal Estimation in the Presence of Noise by Kalman Filter 94\u003c\/p\u003e \u003cp\u003e4.5 Getting Started with Genetic Algorithm Simulation 98\u003c\/p\u003e \u003cp\u003e4.5.1 Genetic Algorithm 98\u003c\/p\u003e \u003cp\u003e4.5.2 Example: Optimizing a Simulink Model by Genetic Algorithm 100\u003c\/p\u003e \u003cp\u003e4.6 Summary 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Expert-System-based Acceleration Control 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 109\u003c\/p\u003e \u003cp\u003e5.2 Relationship between the Stator Voltage Vector and Rotor Acceleration 110\u003c\/p\u003e \u003cp\u003e5.3 Analysis of Motor Acceleration of the Rotor 113\u003c\/p\u003e \u003cp\u003e5.4 Control Strategy of Voltage Vector Comparison and Voltage Vector Retaining 114\u003c\/p\u003e \u003cp\u003e5.5 Expert-System Control for Induction Motor 118\u003c\/p\u003e \u003cp\u003e5.6 Computer Simulation and Comparison 122\u003c\/p\u003e \u003cp\u003e5.6.1 The First Simulation Example 123\u003c\/p\u003e \u003cp\u003e5.6.2 The Second Simulation Example 125\u003c\/p\u003e \u003cp\u003e5.6.3 The Third Simulation Example 126\u003c\/p\u003e \u003cp\u003e5.6.4 The Fourth Simulation Example 127\u003c\/p\u003e \u003cp\u003e5.6.5 The Fifth Simulation Example 129\u003c\/p\u003e \u003cp\u003e5.7 Summary 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Hybrid Fuzzy\/PI Two-Stage Control 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 133\u003c\/p\u003e \u003cp\u003e6.2 Two-Stage Control Strategy for an Induction Motor 135\u003c\/p\u003e \u003cp\u003e6.3 Fuzzy Frequency Control 136\u003c\/p\u003e \u003cp\u003e6.3.1 Fuzzy Database 138\u003c\/p\u003e \u003cp\u003e6.3.2 Fuzzy Rulebase 139\u003c\/p\u003e \u003cp\u003e6.3.3 Fuzzy Inference 141\u003c\/p\u003e \u003cp\u003e6.3.4 Defuzzification 142\u003c\/p\u003e \u003cp\u003e6.3.5 Fuzzy Frequency Controller 142\u003c\/p\u003e \u003cp\u003e6.4 Current Magnitude PI Control 143\u003c\/p\u003e \u003cp\u003e6.5 Hybrid Fuzzy\/PI Two-Stage Controller for an Induction Motor 145\u003c\/p\u003e \u003cp\u003e6.6 Simulation Study on a 7.5 kW Induction Motor 145\u003c\/p\u003e \u003cp\u003e6.6.1 Comparison with Field-Oriented Control 146\u003c\/p\u003e \u003cp\u003e6.6.2 Effects of Parameter Variation 148\u003c\/p\u003e \u003cp\u003e6.6.3 Effects of Noise in the Measured Speed and Input Current 149\u003c\/p\u003e \u003cp\u003e6.6.4 Effects of Magnetic Saturation 149\u003c\/p\u003e \u003cp\u003e6.6.5 Effects of Load Torque Variation 150\u003c\/p\u003e \u003cp\u003e6.7 Simulation Study on a 0.147 kW Induction Motor 152\u003c\/p\u003e \u003cp\u003e6.8 MATLAB\/Simulink Programming Examples 158\u003c\/p\u003e \u003cp\u003e6.8.1 Programming Example 1: Voltage-Input Model of an Induction Motor 158\u003c\/p\u003e \u003cp\u003e6.8.2 Programming Example 2: Fuzzy\/PI Two-Stage Controller 163\u003c\/p\u003e \u003cp\u003e6.9 Summary 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Neural-Network-based Direct Self Control 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 167\u003c\/p\u003e \u003cp\u003e7.2 Neural Networks 168\u003c\/p\u003e \u003cp\u003e7.3 Neural-Network Controller of DSC 170\u003c\/p\u003e \u003cp\u003e7.3.1 Flux Estimation Sub-Net 170\u003c\/p\u003e \u003cp\u003e7.3.2 Torque Calculation Sub-Net 171\u003c\/p\u003e \u003cp\u003e7.3.3 Flux Angle Encoder and Flux Magnitude Calculation Sub-Net 173\u003c\/p\u003e \u003cp\u003e7.3.4 Hysteresis Comparator Sub-Net 178\u003c\/p\u003e \u003cp\u003e7.3.5 Optimum Switching Table Sub-Net 180\u003c\/p\u003e \u003cp\u003e7.3.6 Linking of Neural Networks 183\u003c\/p\u003e \u003cp\u003e7.4 Simulation of Neural-Network-based DSC 184\u003c\/p\u003e \u003cp\u003e7.5 MATLAB\/Simulink Programming Examples 187\u003c\/p\u003e \u003cp\u003e7.5.1 Programming Example 1: Direct Self Controller 187\u003c\/p\u003e \u003cp\u003e7.5.2 Programming Example 2: Neural-Network-based Optimum Switching Table 192\u003c\/p\u003e \u003cp\u003e7.6 Summary 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Parameter Estimation Using Neural Networks 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 199\u003c\/p\u003e \u003cp\u003e8.2 Integral Equations Based on the ‘T’ Equivalent Circuit 200\u003c\/p\u003e \u003cp\u003e8.3 Integral Equations based on the ‘G’ Equivalent Circuit 203\u003c\/p\u003e \u003cp\u003e8.4 Parameter Estimation of Induction Motor Using ANN 205\u003c\/p\u003e \u003cp\u003e8.4.1 Estimation of Electrical Parameters 206\u003c\/p\u003e \u003cp\u003e8.4.2 ANN-based Mechanical Model 208\u003c\/p\u003e \u003cp\u003e8.4.3 Simulation Studies 210\u003c\/p\u003e \u003cp\u003e8.5 ANN-based Induction Motor Models 214\u003c\/p\u003e \u003cp\u003e8.6 Effect of Noise in Training Data on Estimated Parameters 217\u003c\/p\u003e \u003cp\u003e8.7 Estimation of Load, Flux and Speed 218\u003c\/p\u003e \u003cp\u003e8.7.1 Estimation of Load 218\u003c\/p\u003e \u003cp\u003e8.7.2 Estimation of Stator Flux 222\u003c\/p\u003e \u003cp\u003e8.7.3 Estimation of Rotor Speed 226\u003c\/p\u003e \u003cp\u003e8.8 MATLAB\/Simulink Programming Examples 231\u003c\/p\u003e \u003cp\u003e8.8.1 Programming Example 1: Field-Oriented Control (FOC) System 231\u003c\/p\u003e \u003cp\u003e8.8.2 Programming Example 2: Sensorless Control of Induction Motor 234\u003c\/p\u003e \u003cp\u003e8.9 Summary 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 GA-Optimized Extended Kalman Filter for Speed Estimation 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 243\u003c\/p\u003e \u003cp\u003e9.2 Extended State Model of Induction Motor 244\u003c\/p\u003e \u003cp\u003e9.3 Extended Kalman Filter Algorithm for Rotor Speed Estimation 245\u003c\/p\u003e \u003cp\u003e9.3.1 Prediction of State 245\u003c\/p\u003e \u003cp\u003e9.3.2 Estimation of Error Covariance Matrix 245\u003c\/p\u003e \u003cp\u003e9.3.3 Computation of Kalman Filter Gain 245\u003c\/p\u003e \u003cp\u003e9.3.4 State Estimation 246\u003c\/p\u003e \u003cp\u003e9.3.5 Update of the Error Covariance Matrix 246\u003c\/p\u003e \u003cp\u003e9.4 Optimized Extended Kalman Filter 247\u003c\/p\u003e \u003cp\u003e9.5 Optimizing the Noise Matrices of EKF Using GA 250\u003c\/p\u003e \u003cp\u003e9.6 Speed Estimation for a Sensorless Direct Self Controller 253\u003c\/p\u003e \u003cp\u003e9.7 Speed Estimation for a Field-Oriented Controller 255\u003c\/p\u003e \u003cp\u003e9.8 MATLAB\/Simulink Programming Examples 260\u003c\/p\u003e \u003cp\u003e9.8.1 Programming Example 1: Voltage-Frequency Controlled (VFC) Drive 260\u003c\/p\u003e \u003cp\u003e9.8.2 Programming Example 2: GA-Optimized EKF for Speed Estimation 264\u003c\/p\u003e \u003cp\u003e9.8.3 Programming Example 3: GA-based EKF Sensorless Voltage-Frequency Controlled Drive 268\u003c\/p\u003e \u003cp\u003e9.8.4 Programming Example 4: GA-based EKF Sensorless FOC Induction Motor Drive 269\u003c\/p\u003e \u003cp\u003e9.9 Summary 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimized Random PWM Strategies Based On Genetic Algorithms 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 273\u003c\/p\u003e \u003cp\u003e10.2 PWM Performance Evaluation 274\u003c\/p\u003e \u003cp\u003e10.2.1 Fourier Analysis of PWM Waveform 276\u003c\/p\u003e \u003cp\u003e10.2.2 Harmonic Evaluation of Typical Waveforms 277\u003c\/p\u003e \u003cp\u003e10.3 Random PWM Methods 283\u003c\/p\u003e \u003cp\u003e10.3.1 Random Carrier-Frequency PWM 283\u003c\/p\u003e \u003cp\u003e10.3.2 Random Pulse-Position PWM 285\u003c\/p\u003e \u003cp\u003e10.3.3 Random Pulse-Width PWM 285\u003c\/p\u003e \u003cp\u003e10.3.4 Hybrid Random Pulse-Position and Pulse-Width PWM 286\u003c\/p\u003e \u003cp\u003e10.3.5 Harmonic Evaluation Results 287\u003c\/p\u003e \u003cp\u003e10.4 Optimized Random PWM Based on Genetic Algorithm 288\u003c\/p\u003e \u003cp\u003e10.4.1 GA-Optimized Random Carrier-Frequency PWM 289\u003c\/p\u003e \u003cp\u003e10.4.2 GA-Optimized Random-Pulse-Position PWM 290\u003c\/p\u003e \u003cp\u003e10.4.3 GA-Optimized Random-Pulse-Width PWM 292\u003c\/p\u003e \u003cp\u003e10.4.4 GA-Optimized Hybrid Random Pulse-Position and Pulse-Width PWM 293\u003c\/p\u003e \u003cp\u003e10.4.5 Evaluation of Various GA-Optimized Random PWM Inverters 295\u003c\/p\u003e \u003cp\u003e10.4.6 Switching Loss of GA-Optimized Random Single-Phase PWM Inverters 296\u003c\/p\u003e \u003cp\u003e10.4.7 Linear Modulation Range of GA-Optimized Random Single-Phase PWM Inverters 297\u003c\/p\u003e \u003cp\u003e10.4.8 Implementation of GA-Optimized Random Single-Phase PWM Inverter 298\u003c\/p\u003e \u003cp\u003e10.4.9 Limitations of Reference Sinusoidal Frequency of GA-Optimized Random PWM Inverters 298\u003c\/p\u003e \u003cp\u003e10.5 MATLAB\/Simulink Programming Examples 299\u003c\/p\u003e \u003cp\u003e10.5.1 Programming Example 1: A Single-Phase Sinusoidal PWM 299\u003c\/p\u003e \u003cp\u003e10.5.2 Programming Example 2: Evaluation of a Four-Pulse Wave 302\u003c\/p\u003e \u003cp\u003e10.5.3 Programming Example 3: Random Carrier-Frequency\u003c\/p\u003e \u003cp\u003e10.6 Experiments on Various PWM Strategies 305\u003c\/p\u003e \u003cp\u003e10.6.1 Implementation of PWM Methods Using DSP 305\u003c\/p\u003e \u003cp\u003e10.6.2 Experimental Results 307\u003c\/p\u003e \u003cp\u003e10.7 Summary 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Experimental Investigations 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 313\u003c\/p\u003e \u003cp\u003e11.2 Experimental Hardware Design for Induction Motor Control 314\u003c\/p\u003e \u003cp\u003e11.2.1 Hardware Description 314\u003c\/p\u003e \u003cp\u003e11.3 Software Development Method 320\u003c\/p\u003e \u003cp\u003e11.4 Experiment 1: Determination of Motor Parameters 321\u003c\/p\u003e \u003cp\u003e11.5 Experiment 2: Induction Motor Run Up 321\u003c\/p\u003e \u003cp\u003e11.5.1 Program Design 322\u003c\/p\u003e \u003cp\u003e11.5.2 Program Debug 324\u003c\/p\u003e \u003cp\u003e11.5.3 Experimental Investigations 327\u003c\/p\u003e \u003cp\u003e11.6 Experiment 3: Implementation of Fuzzy\/PI Two-Stage Controller 330\u003c\/p\u003e \u003cp\u003e11.6.1 Program Design 330\u003c\/p\u003e \u003cp\u003e11.6.2 Program Debug 338\u003c\/p\u003e \u003cp\u003e11.6.3 Performance Tests 339\u003c\/p\u003e \u003cp\u003e11.7 Experiment 4: Speed Estimation Using a GA-Optimized Extended Kalman Filter 344\u003c\/p\u003e \u003cp\u003e11.7.1 Program Design 345\u003c\/p\u003e \u003cp\u003e11.7.2 GA-EKF Experimental Method 345\u003c\/p\u003e \u003cp\u003e11.7.3 GA-EKF Experiments 346\u003c\/p\u003e \u003cp\u003e11.7.4 Limitations of GA-EKF 349\u003c\/p\u003e \u003cp\u003e11.8 DSP Programming Examples 352\u003c\/p\u003e \u003cp\u003e11.8.1 Generation of 3-Phase Sinusoidal PWM 354\u003c\/p\u003e \u003cp\u003e11.8.2 RTDX Programming 359\u003c\/p\u003e \u003cp\u003e11.8.3 ADC Programming 361\u003c\/p\u003e \u003cp\u003e11.8.4 CAP Programming 364\u003c\/p\u003e \u003cp\u003e11.9 Summary 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Conclusions and Future Developments 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Main Contributions of the Book 374\u003c\/p\u003e \u003cp\u003e12.2 Industrial Applications of New Induction Motor Drives 375\u003c\/p\u003e \u003cp\u003e12.3 Future Developments 377\u003c\/p\u003e \u003cp\u003e12.3.1 Expert-System-based Acceleration Control 378\u003c\/p\u003e \u003cp\u003e12.3.2 Hybrid Fuzzy\/PI Two-Stage Control 378\u003c\/p\u003e \u003cp\u003e12.3.3 Neural-Network-based Direct Self Control 378\u003c\/p\u003e \u003cp\u003e12.3.4 Genetic Algorithm for an Extended Kalman Filter 378\u003c\/p\u003e \u003cp\u003e12.3.5 Parameter Estimation Using Neural Networks 378\u003c\/p\u003e \u003cp\u003e12.3.6 Optimized Random PWM Strategies Based on Genetic Algorithms 378\u003c\/p\u003e \u003cp\u003e12.3.7 AI-Integrated Algorithm and Hardware 379\u003c\/p\u003e \u003cp\u003eAppendix A Equivalent Circuits of an Induction Motor 381\u003c\/p\u003e \u003cp\u003eAppendix B Parameters of Induction Motors 383\u003c\/p\u003e \u003cp\u003eAppendix C M-File of Discrete-State Induction Motor Model 385\u003c\/p\u003e \u003cp\u003eAppendix D Expert-System Acceleration Control Algorithm 387\u003c\/p\u003e \u003cp\u003eAppendix E Activation Functions of Neural Network 391\u003c\/p\u003e \u003cp\u003eAppendix F M-File of Extended Kalman Filter 393\u003c\/p\u003e \u003cp\u003eAppendix G ADMC331-based Experimental System 395\u003c\/p\u003e \u003cp\u003eAppendix H Experiment 1: Measuring the Electrical Parameters of Motor 3 397\u003c\/p\u003e \u003cp\u003eAppendix I DSP Source Code for the Main Program of Experiment 2 403\u003c\/p\u003e \u003cp\u003eAppendix J DSP Source Code for the Main Program of Experiment 3 407\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default 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