Info
PID and predictive control of electrical drives and power converters using Matlab®/Simulink®/ / Liuping Wang, Shan Chai, Dae Yoo, Lu Gan and Ki Ng
| PID and predictive control of electrical drives and power converters using Matlab®/Simulink®/ / Liuping Wang, Shan Chai, Dae Yoo, Lu Gan and Ki Ng |
| Autore | Wang Liuping |
| Pubbl/distr/stampa | Solaris South Tower, Singapore : , : John Wiley & Sons, Inc., , [2015] |
| Descrizione fisica | 1 online resource (370 p.) |
| Disciplina | 621.46 |
| Soggetto topico |
PID controllers
Electric motors - Electronic control Electric power supplies to apparatus - Automatic control |
| ISBN |
1-118-33947-9
1-118-33945-2 1-118-33946-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
About the Authors xiii -- Preface xv -- Acknowledgment xix -- List of Symbols and Acronyms xxi -- 1 Modeling of AC Drives and Power Converter 1 -- 1.1 Space Phasor Representation 1 -- 1.1.1 Space Vector for Magnetic Motive Force 1 -- 1.1.2 Space Vector Representation of Voltage Equation 4 -- 1.2 Model of Surface Mounted PMSM 5 -- 1.2.1 Representation in Stationary Reference Frame 5 -- 1.2.2 Representation in Synchronous Reference Frame 7 -- 1.2.3 Electromagnetic Torque 8 -- 1.3 Model of Interior Magnets PMSM 10 -- 1.3.1 Complete Model of PMSM 11 -- 1.4 Per Unit Model and PMSM Parameters 11 -- 1.4.1 Per Unit Model and Physical Parameters 11 -- 1.4.2 Experimental Validation of PMSM Model 12 -- 1.5 Modeling of Induction Motor 13 -- 1.5.1 Space Vector Representation of Voltage Equation of Induction Motor 13 -- 1.5.2 Representation in Stationary Reference Frame 17 -- 1.5.3 Representation in Reference Frame 17 -- 1.5.4 Electromagnetic Torque of Induction Motor 19 -- 1.5.5 Model Parameters of Induction Motor and Model Validation 19 -- 1.6 Modeling of Power Converter 21 -- 1.6.1 Space Vector Representation of Voltage Equation for Power Converter 22 -- 1.6.2 Representation in Reference Frame 22 -- 1.6.3 Representation in Reference Frame 23 -- 1.6.4 Energy Balance Equation 24 -- 1.7 Summary 25 -- 1.8 Further Reading 25 -- References 25 -- 2 Control of Semiconductor Switches via PWM Technologies 27 -- 2.1 Topology of IGBT Inverter 28 -- 2.2 Six-step Operating Mode 30 -- 2.3 Carrier Based PWM 31 -- 2.3.1 Sinusoidal PWM 31 -- 2.3.2 Carrier Based PWM with Zero-sequence Injection 32 -- 2.4 Space Vector PWM 35 -- 2.5 Simulation Study of the Effect of PWM 37 -- 2.6 Summary 40 -- 2.7 Further Reading 40 -- References 40 -- 3 PID Control System Design for Electrical Drives and Power Converters 41 -- 3.1 Overview of PID Control Systems Using Pole-assignment Design Techniques 42 -- 3.1.1 PI Controller Design 42 -- 3.1.2 Selecting the Desired Closed-loop Performance 43 -- 3.1.3 Overshoot in Reference Response 45.
3.1.4 PID Controller Design 46 -- 3.1.5 Cascade PID Control Systems 48 -- 3.2 Overview of PID Control of PMSM 49 -- 3.2.1 Bridging the Sensor Measurements to Feedback Signals (See the lower part of Figure 3.6) 50 -- 3.2.2 Bridging the Control Signals to the Inputs to the PMSM (See the top part of Figure 3.6) 51 -- 3.3 PI Controller Design for Torque Control of PMSM 52 -- 3.3.1 Set-point Signals to the Current Control Loops 52 -- 3.3.2 Decoupling of the Current Control Systems 53 -- 3.3.3 PI Current Controller Design 54 -- 3.4 Velocity Control of PMSM 55 -- 3.4.1 Inner-loop Proportional Control of q-axis Current 55 -- 3.4.2 Cascade Feedback Control of Velocity:P Plus PI 57 -- 3.4.3 Simulation Example for P Plus PI Control System 59 -- 3.4.4 Cascade Feedback Control of Velocity:PI Plus PI 61 -- 3.4.5 Simulation Example for PI Plus PI Control System 63 -- 3.5 PID Controller Design for Position Control of PMSM 64 -- 3.6 Overview of PID Control of Induction Motor 65 -- 3.6.1 Bridging the Sensor Measurements to Feedback Signals 67 -- 3.6.2 Bridging the Control Signals to the Inputs to the Induction Motor 67 -- 3.7 PID Controller Design for Induction Motor 68 -- 3.7.1 PI Control of Electromagnetic Torque of Induction Motor 68 -- 3.7.2 Cascade Control of Velocity and Position 70 -- 3.7.3 Slip Estimation 73 -- 3.8 Overview of PID Control of Power Converter 74 -- 3.8.1 Bridging Sensor Measurements to Feedback Signals 75 -- 3.8.2 Bridging the Control Signals to the Inputs of the Power Converter 76 -- 3.9 PI Current and Voltage Controller Design for Power Converter 76 -- 3.9.1 P Control of d-axis Current 76 -- 3.9.2 PI Control of q-axis Current 77 -- 3.9.3 PI Cascade Control of Output Voltage 79 -- 3.9.4 Simulation Example 80 -- 3.9.5 Phase Locked Loop 80 -- 3.10 Summary 82 -- 3.11 Further Reading 83 -- References 83 -- 4 PID Control System Implementation 87 -- 4.1 P and PI Controller Implementation in Current Control Systems 87 -- 4.1.1 Voltage Operational Limits in Current Control Systems 87. 4.1.2 Discretization of Current Controllers 90 -- 4.1.3 Anti-windup Mechanisms 92 -- 4.2 Implementation of Current Controllers for PMSM 93 -- 4.3 Implementation of Current Controllers for Induction Motors 95 -- 4.4 Current Controller Implementation for Power Converter 97 -- 4.4.1 Constraints on the Control Variables 97 -- 4.5 Implementation of Outer-loop PI Control System 98 -- 4.5.1 Constraints in the Outer-loop 98 -- 4.5.2 Over Current Protection for AC Machines 99 -- 4.5.3 Implementation of Outer-loop PI Control of Velocity 100 -- 4.5.4 Over Current Protection for Power Converters 100 -- 4.6 MATLAB Tutorial on Implementation of PI Controller 100 -- 4.7 Summary 102 -- 4.8 Further Reading 103 -- References 103 -- 5 Tuning PID Control Systems with Experimental Validations 105 -- 5.1 Sensitivity Functions in Feedback Control Systems 105 -- 5.1.1 Two-degrees of Freedom Control System Structure 105 -- 5.1.2 Sensitivity Functions 109 -- 5.1.3 Disturbance Rejection and Noise Attenuation 110 -- 5.2 Tuning Current-loop q-axis Proportional Controller (PMSM) 111 -- 5.2.1 Performance Factor and Proportional Gain 112 -- 5.2.2 Complementary Sensitivity Function 112 -- 5.2.3 Sensitivity and Input Sensitivity Functions 114 -- 5.2.4 Effect of PWM Noise on Current Proportional Control System 114 -- 5.2.5 Effect of Current Sensor Noise and Bias 116 -- 5.2.6 Experimental Case Study of Current Sensor Bias Using P Control 118 -- 5.2.7 Experimental Case Study of Current Loop Noise 119 -- 5.3 Tuning Current-loop PI Controller (PMSM) 123 -- 5.4 Performance Robustness in Outer-loop Controllers 128 -- 5.4.1 Sensitivity Functions for Outer-loop Control System 131 -- 5.4.2 Input Sensitivity Functions for the Outer-loop System 135 -- 5.5 Analysis of Time-delay Effects 136 -- 5.5.1 PI Control of q-axis Current 137 -- 5.5.2 P Control of q-axis Current 137 -- 5.6 Tuning Cascade PI Control Systems for Induction Motor 138 -- 5.6.1 Robustness of Cascade PI Control System 140 -- 5.6.2 Robustness Study Using Nyquist Plot 143. 5.7 Tuning PI Control Systems for Power Converter 147 -- 5.7.1 Overview of the Designs 147 -- 5.7.2 Tuning the Current Controllers 149 -- 5.7.3 Tuning Voltage Controller 150 -- 5.7.4 Experimental Evaluations 154 -- 5.8 Tuning P Plus PI Controllers for Power Converter 157 -- 5.8.1 Design and Sensitivity Functions 157 -- 5.8.2 Experimental Results 158 -- 5.9 Robustness of Power Converter Control System Using PI Current Controllers 159 -- 5.9.1 Variation of Inductance Using PI Current Controllers 160 -- 5.9.2 Variation of Capacitance on Closed-loop Performance 163 -- 5.10 Summary 167 -- 5.10.1 Current Controllers 167 -- 5.10.2 Velocity, Position and Voltage Controllers 168 -- 5.10.3 Choice between P Current Control and PI Current Control 169 -- 5.11 Further Reading 169 -- References 169 -- 6 FCS Predictive Control in d - q Reference Frame 171 -- 6.1 States of IGBT Inverter and the Operational Constraints 172 -- 6.2 FCS Predictive Control of PMSM 175 -- 6.3 MATLAB Tutorial on Real-time Implementation of FCS-MPC 177 -- 6.3.1 Simulation Results 179 -- 6.3.2 Experimental Results of FCS Control 181 -- 6.4 Analysis of FCS-MPC System 182 -- 6.4.1 Optimal Control System 182 -- 6.4.2 Feedback Controller Gain 184 -- 6.4.3 Constrained Optimal Control 185 -- 6.5 Overview of FCS-MPC with Integral Action 187 -- 6.6 Derivation of I-FCS Predictive Control Algorithm 191 -- 6.6.1 Optimal Control without Constraints 191 -- 6.6.2 I-FCS Predictive Controller with Constraints 194 -- 6.6.3 Implementation of I-FCS-MPC Algorithm 196 -- 6.7 MATLAB Tutorial on Implementation of I-FCS Predictive Controller 197 -- 6.7.1 Simulation Results 198 -- 6.8 I-FCS Predictive Control of Induction Motor 201 -- 6.8.1 The Control Algorithm for an Induction Motor 202 -- 6.8.2 Simulation Results 204 -- 6.8.3 Experimental Results 205 -- 6.9 I-FCS Predictive Control of Power Converter 209 -- 6.9.1 I-FCS Predictive Control of a Power Converter 209 -- 6.9.2 Simulation Results 211 -- 6.9.3 Experimental Results 214. 6.10 Evaluation of Robustness of I-FCS-MPC via Monte-Carlo Simulations 215 -- 6.10.1 Discussion on Mean Square Errors 216 -- 6.11 Velocity and Position Control of PMSM Using I-FCS-MPC 218 -- 6.11.1 Choice of Sampling Rate for the Outer-loop Control System 219 -- 6.11.2 Velocity and Position Controller Design 223 -- 6.12 Velocity and Position Control of Induction Motor Using I-FCS-MPC 224 -- 6.12.1 I-FCS Cascade Velocity Control of Induction Motor 225 -- 6.12.2 I-FCS-MPC Cascade Position Control of Induction Motor 226 -- 6.12.3 Experimental Evaluation of Velocity Control 228 -- 6.13 Summary 232 -- 6.13.1 Selection of sampling interval 233 -- 6.13.2 Selection of the Integral Gain 233 -- 6.14 Further Reading 234 -- References 234 -- 7 FCS Predictive Control in Reference Frame 237 -- 7.1 FCS Predictive Current Control of PMSM 237 -- 7.1.1 Predictive Control Using One-step-ahead Prediction 238 -- 7.1.2 FCS Current Control in Reference Frame 239 -- 7.1.3 Generating Current Reference Signals in Frame 240 -- 7.2 Resonant FCS Predictive Current Control 241 -- 7.2.1 Control System Configuration 241 -- 7.2.2 Outer-loop Controller Design 242 -- 7.2.3 Resonant FCS Predictive Control System 243 -- 7.3 Resonant FCS Current Control of Induction Motor 247 -- 7.3.1 The Original FCS Current Control of Induction Motor 247 -- 7.3.2 Resonant FCS Predictive Current Control of Induction Motor 250 -- 7.3.3 Experimental Evaluations of Resonant FCS Predictive Control 252 -- 7.4 Resonant FCS Predictive Power Converter Control 255 -- 7.4.1 FCS Predictive Current Control of Power Converter 255 -- 7.4.2 Experimental Results of Resonant FCS Predictive Control 260 -- 7.5 Summary 261 -- 7.6 Further Reading 262 -- References 262 -- 8 Discrete-time Model Predictive Control (DMPC) of Electrical Drives and Power Converter 265 -- 8.1 Linear Discrete-time Model for PMSM 266 -- 8.1.1 Linear Model for PMSM 266 -- 8.1.2 Discretization of the Continuous-time Model 267 -- 8.2 Discrete-time MPC Design with Constraints 268. 8.2.1 Augmented Model 269 -- 8.2.2 Design without Constraints 270 -- 8.2.3 Formulation of the Constraints 272 -- 8.2.4 On-line Solution for Constrained MPC 272 -- 8.3 Experimental Evaluation of DMPC of PMSM 274 -- 8.3.1 The MPC Parameters 274 -- 8.3.2 Constraints 275 -- 8.3.3 Response to Load Disturbances 275 -- 8.3.4 Response to a Staircase Reference 277 -- 8.3.5 Tuning of the MPC controller 278 -- 8.4 Power Converter Control Using DMPC with Experimental Validation 280 -- 8.5 Summary 281 -- 8.6 Further Reading 282 -- References 283 -- 9 Continuous-time Model Predictive Control (CMPC) of Electrical Drives and PowerConverter 285 -- 9.1 Continuous-time MPC Design 286 -- 9.1.1 Augmented Model 286 -- 9.1.2 Description of the Control Trajectories Using Laguerre Functions 287 -- 9.1.3 Continuous-time Predictive Control without Constraints 289 -- 9.1.4 Tuning of CMPC Control System Using Exponential Data Weighting and Prescribed Degree of Stability 292 -- 9.2 CMPC with Nonlinear Constraints 294 -- 9.2.1 Approximation of Nonlinear Constraint Using Four Linear Constraints 294 -- 9.2.2 Approximation of Nonlinear Constraint Using Sixteen Linear Constraints 294 -- 9.2.3 State Feedback Observer 297 -- 9.3 Simulation and Experimental Evaluation of CMPC of Induction Motor 298 -- 9.3.1 Simulation Results 298 -- 9.3.2 Experimental Results 300 -- 9.4 Continuous-time Model Predictive Control of Power Converter 301 -- 9.4.1 Use of Prescribed Degree of Stability in the Design 302 -- 9.4.2 Experimental Results for Rectification Mode 303 -- 9.4.3 Experimental Results for Regeneration Mode 303 -- 9.4.4 Experimental Results for Disturbance Rejection 304 -- 9.5 Gain Scheduled Predictive Controller 305 -- 9.5.1 The Weighting Parameters 305 -- 9.5.2 Gain Scheduled Predictive Control Law 307 -- 9.6 Experimental Results of Gain Scheduled Predictive Control of Induction Motor 309 -- 9.6.1 The First Set of Experimental Results 309 -- 9.6.2 The Second Set of Experimental Results 311 -- 9.6.3 The Third Set of Experimental Results 312. 9.7 Summary 312 -- 9.8 Further Reading 313 -- References 313 -- 10 MATLAB(R)/Simulink(R) Tutorials on Physical Modeling and Test-bed Setup 315 -- 10.1 Building Embedded Functions for Park-Clarke Transformation 315 -- 10.1.1 Park-Clarke Transformation for Current Measurements 316 -- 10.1.2 Inverse Park-Clarke Transformation for Voltage Actuation 317 -- 10.2 Building Simulation Model for PMSM 318 -- 10.3 Building Simulation Model for Induction Motor 320 -- 10.4 Building Simulation Model for Power Converter 325 -- 10.4.1 Embedded MATLAB Function for Phase Locked Loop (PLL) 325 -- 10.4.2 Physical Simulation Model for Grid Connected Voltage Source Converter 328 -- 10.5 PMSM Experimental Setup 332 -- 10.6 Induction Motor Experimental Setup 334 -- 10.6.1 Controller 334 -- 10.6.2 Power Supply 334 -- 10.6.3 Inverter 335 -- 10.6.4 Mechanical Load 335 -- 10.6.5 Induction Motor and Sensors 335 -- 10.7 Grid Connected Power Converter Experimental Setup 335 -- 10.7.1 Controller 335 -- 10.7.2 Inverter 336 -- 10.7.3 Sensors 336 -- 10.8 Summary 337 -- 10.9 Further Reading 337 -- References 337 -- Index 339. |
| Record Nr. | UNINA-9910132301603321 |
Wang Liuping
|
||
| Solaris South Tower, Singapore : , : John Wiley & Sons, Inc., , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
PID and predictive control of electrical drives and power converters using Matlab®/Simulink®/ / Liuping Wang, Shan Chai, Dae Yoo, Lu Gan and Ki Ng
| PID and predictive control of electrical drives and power converters using Matlab®/Simulink®/ / Liuping Wang, Shan Chai, Dae Yoo, Lu Gan and Ki Ng |
| Autore | Wang Liuping |
| Pubbl/distr/stampa | Solaris South Tower, Singapore : , : John Wiley & Sons, Inc., , [2015] |
| Descrizione fisica | 1 online resource (370 p.) |
| Disciplina | 621.46 |
| Soggetto topico |
PID controllers
Electric motors - Electronic control Electric power supplies to apparatus - Automatic control |
| ISBN |
1-118-33947-9
1-118-33945-2 1-118-33946-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
About the Authors xiii -- Preface xv -- Acknowledgment xix -- List of Symbols and Acronyms xxi -- 1 Modeling of AC Drives and Power Converter 1 -- 1.1 Space Phasor Representation 1 -- 1.1.1 Space Vector for Magnetic Motive Force 1 -- 1.1.2 Space Vector Representation of Voltage Equation 4 -- 1.2 Model of Surface Mounted PMSM 5 -- 1.2.1 Representation in Stationary Reference Frame 5 -- 1.2.2 Representation in Synchronous Reference Frame 7 -- 1.2.3 Electromagnetic Torque 8 -- 1.3 Model of Interior Magnets PMSM 10 -- 1.3.1 Complete Model of PMSM 11 -- 1.4 Per Unit Model and PMSM Parameters 11 -- 1.4.1 Per Unit Model and Physical Parameters 11 -- 1.4.2 Experimental Validation of PMSM Model 12 -- 1.5 Modeling of Induction Motor 13 -- 1.5.1 Space Vector Representation of Voltage Equation of Induction Motor 13 -- 1.5.2 Representation in Stationary Reference Frame 17 -- 1.5.3 Representation in Reference Frame 17 -- 1.5.4 Electromagnetic Torque of Induction Motor 19 -- 1.5.5 Model Parameters of Induction Motor and Model Validation 19 -- 1.6 Modeling of Power Converter 21 -- 1.6.1 Space Vector Representation of Voltage Equation for Power Converter 22 -- 1.6.2 Representation in Reference Frame 22 -- 1.6.3 Representation in Reference Frame 23 -- 1.6.4 Energy Balance Equation 24 -- 1.7 Summary 25 -- 1.8 Further Reading 25 -- References 25 -- 2 Control of Semiconductor Switches via PWM Technologies 27 -- 2.1 Topology of IGBT Inverter 28 -- 2.2 Six-step Operating Mode 30 -- 2.3 Carrier Based PWM 31 -- 2.3.1 Sinusoidal PWM 31 -- 2.3.2 Carrier Based PWM with Zero-sequence Injection 32 -- 2.4 Space Vector PWM 35 -- 2.5 Simulation Study of the Effect of PWM 37 -- 2.6 Summary 40 -- 2.7 Further Reading 40 -- References 40 -- 3 PID Control System Design for Electrical Drives and Power Converters 41 -- 3.1 Overview of PID Control Systems Using Pole-assignment Design Techniques 42 -- 3.1.1 PI Controller Design 42 -- 3.1.2 Selecting the Desired Closed-loop Performance 43 -- 3.1.3 Overshoot in Reference Response 45.
3.1.4 PID Controller Design 46 -- 3.1.5 Cascade PID Control Systems 48 -- 3.2 Overview of PID Control of PMSM 49 -- 3.2.1 Bridging the Sensor Measurements to Feedback Signals (See the lower part of Figure 3.6) 50 -- 3.2.2 Bridging the Control Signals to the Inputs to the PMSM (See the top part of Figure 3.6) 51 -- 3.3 PI Controller Design for Torque Control of PMSM 52 -- 3.3.1 Set-point Signals to the Current Control Loops 52 -- 3.3.2 Decoupling of the Current Control Systems 53 -- 3.3.3 PI Current Controller Design 54 -- 3.4 Velocity Control of PMSM 55 -- 3.4.1 Inner-loop Proportional Control of q-axis Current 55 -- 3.4.2 Cascade Feedback Control of Velocity:P Plus PI 57 -- 3.4.3 Simulation Example for P Plus PI Control System 59 -- 3.4.4 Cascade Feedback Control of Velocity:PI Plus PI 61 -- 3.4.5 Simulation Example for PI Plus PI Control System 63 -- 3.5 PID Controller Design for Position Control of PMSM 64 -- 3.6 Overview of PID Control of Induction Motor 65 -- 3.6.1 Bridging the Sensor Measurements to Feedback Signals 67 -- 3.6.2 Bridging the Control Signals to the Inputs to the Induction Motor 67 -- 3.7 PID Controller Design for Induction Motor 68 -- 3.7.1 PI Control of Electromagnetic Torque of Induction Motor 68 -- 3.7.2 Cascade Control of Velocity and Position 70 -- 3.7.3 Slip Estimation 73 -- 3.8 Overview of PID Control of Power Converter 74 -- 3.8.1 Bridging Sensor Measurements to Feedback Signals 75 -- 3.8.2 Bridging the Control Signals to the Inputs of the Power Converter 76 -- 3.9 PI Current and Voltage Controller Design for Power Converter 76 -- 3.9.1 P Control of d-axis Current 76 -- 3.9.2 PI Control of q-axis Current 77 -- 3.9.3 PI Cascade Control of Output Voltage 79 -- 3.9.4 Simulation Example 80 -- 3.9.5 Phase Locked Loop 80 -- 3.10 Summary 82 -- 3.11 Further Reading 83 -- References 83 -- 4 PID Control System Implementation 87 -- 4.1 P and PI Controller Implementation in Current Control Systems 87 -- 4.1.1 Voltage Operational Limits in Current Control Systems 87. 4.1.2 Discretization of Current Controllers 90 -- 4.1.3 Anti-windup Mechanisms 92 -- 4.2 Implementation of Current Controllers for PMSM 93 -- 4.3 Implementation of Current Controllers for Induction Motors 95 -- 4.4 Current Controller Implementation for Power Converter 97 -- 4.4.1 Constraints on the Control Variables 97 -- 4.5 Implementation of Outer-loop PI Control System 98 -- 4.5.1 Constraints in the Outer-loop 98 -- 4.5.2 Over Current Protection for AC Machines 99 -- 4.5.3 Implementation of Outer-loop PI Control of Velocity 100 -- 4.5.4 Over Current Protection for Power Converters 100 -- 4.6 MATLAB Tutorial on Implementation of PI Controller 100 -- 4.7 Summary 102 -- 4.8 Further Reading 103 -- References 103 -- 5 Tuning PID Control Systems with Experimental Validations 105 -- 5.1 Sensitivity Functions in Feedback Control Systems 105 -- 5.1.1 Two-degrees of Freedom Control System Structure 105 -- 5.1.2 Sensitivity Functions 109 -- 5.1.3 Disturbance Rejection and Noise Attenuation 110 -- 5.2 Tuning Current-loop q-axis Proportional Controller (PMSM) 111 -- 5.2.1 Performance Factor and Proportional Gain 112 -- 5.2.2 Complementary Sensitivity Function 112 -- 5.2.3 Sensitivity and Input Sensitivity Functions 114 -- 5.2.4 Effect of PWM Noise on Current Proportional Control System 114 -- 5.2.5 Effect of Current Sensor Noise and Bias 116 -- 5.2.6 Experimental Case Study of Current Sensor Bias Using P Control 118 -- 5.2.7 Experimental Case Study of Current Loop Noise 119 -- 5.3 Tuning Current-loop PI Controller (PMSM) 123 -- 5.4 Performance Robustness in Outer-loop Controllers 128 -- 5.4.1 Sensitivity Functions for Outer-loop Control System 131 -- 5.4.2 Input Sensitivity Functions for the Outer-loop System 135 -- 5.5 Analysis of Time-delay Effects 136 -- 5.5.1 PI Control of q-axis Current 137 -- 5.5.2 P Control of q-axis Current 137 -- 5.6 Tuning Cascade PI Control Systems for Induction Motor 138 -- 5.6.1 Robustness of Cascade PI Control System 140 -- 5.6.2 Robustness Study Using Nyquist Plot 143. 5.7 Tuning PI Control Systems for Power Converter 147 -- 5.7.1 Overview of the Designs 147 -- 5.7.2 Tuning the Current Controllers 149 -- 5.7.3 Tuning Voltage Controller 150 -- 5.7.4 Experimental Evaluations 154 -- 5.8 Tuning P Plus PI Controllers for Power Converter 157 -- 5.8.1 Design and Sensitivity Functions 157 -- 5.8.2 Experimental Results 158 -- 5.9 Robustness of Power Converter Control System Using PI Current Controllers 159 -- 5.9.1 Variation of Inductance Using PI Current Controllers 160 -- 5.9.2 Variation of Capacitance on Closed-loop Performance 163 -- 5.10 Summary 167 -- 5.10.1 Current Controllers 167 -- 5.10.2 Velocity, Position and Voltage Controllers 168 -- 5.10.3 Choice between P Current Control and PI Current Control 169 -- 5.11 Further Reading 169 -- References 169 -- 6 FCS Predictive Control in d - q Reference Frame 171 -- 6.1 States of IGBT Inverter and the Operational Constraints 172 -- 6.2 FCS Predictive Control of PMSM 175 -- 6.3 MATLAB Tutorial on Real-time Implementation of FCS-MPC 177 -- 6.3.1 Simulation Results 179 -- 6.3.2 Experimental Results of FCS Control 181 -- 6.4 Analysis of FCS-MPC System 182 -- 6.4.1 Optimal Control System 182 -- 6.4.2 Feedback Controller Gain 184 -- 6.4.3 Constrained Optimal Control 185 -- 6.5 Overview of FCS-MPC with Integral Action 187 -- 6.6 Derivation of I-FCS Predictive Control Algorithm 191 -- 6.6.1 Optimal Control without Constraints 191 -- 6.6.2 I-FCS Predictive Controller with Constraints 194 -- 6.6.3 Implementation of I-FCS-MPC Algorithm 196 -- 6.7 MATLAB Tutorial on Implementation of I-FCS Predictive Controller 197 -- 6.7.1 Simulation Results 198 -- 6.8 I-FCS Predictive Control of Induction Motor 201 -- 6.8.1 The Control Algorithm for an Induction Motor 202 -- 6.8.2 Simulation Results 204 -- 6.8.3 Experimental Results 205 -- 6.9 I-FCS Predictive Control of Power Converter 209 -- 6.9.1 I-FCS Predictive Control of a Power Converter 209 -- 6.9.2 Simulation Results 211 -- 6.9.3 Experimental Results 214. 6.10 Evaluation of Robustness of I-FCS-MPC via Monte-Carlo Simulations 215 -- 6.10.1 Discussion on Mean Square Errors 216 -- 6.11 Velocity and Position Control of PMSM Using I-FCS-MPC 218 -- 6.11.1 Choice of Sampling Rate for the Outer-loop Control System 219 -- 6.11.2 Velocity and Position Controller Design 223 -- 6.12 Velocity and Position Control of Induction Motor Using I-FCS-MPC 224 -- 6.12.1 I-FCS Cascade Velocity Control of Induction Motor 225 -- 6.12.2 I-FCS-MPC Cascade Position Control of Induction Motor 226 -- 6.12.3 Experimental Evaluation of Velocity Control 228 -- 6.13 Summary 232 -- 6.13.1 Selection of sampling interval 233 -- 6.13.2 Selection of the Integral Gain 233 -- 6.14 Further Reading 234 -- References 234 -- 7 FCS Predictive Control in Reference Frame 237 -- 7.1 FCS Predictive Current Control of PMSM 237 -- 7.1.1 Predictive Control Using One-step-ahead Prediction 238 -- 7.1.2 FCS Current Control in Reference Frame 239 -- 7.1.3 Generating Current Reference Signals in Frame 240 -- 7.2 Resonant FCS Predictive Current Control 241 -- 7.2.1 Control System Configuration 241 -- 7.2.2 Outer-loop Controller Design 242 -- 7.2.3 Resonant FCS Predictive Control System 243 -- 7.3 Resonant FCS Current Control of Induction Motor 247 -- 7.3.1 The Original FCS Current Control of Induction Motor 247 -- 7.3.2 Resonant FCS Predictive Current Control of Induction Motor 250 -- 7.3.3 Experimental Evaluations of Resonant FCS Predictive Control 252 -- 7.4 Resonant FCS Predictive Power Converter Control 255 -- 7.4.1 FCS Predictive Current Control of Power Converter 255 -- 7.4.2 Experimental Results of Resonant FCS Predictive Control 260 -- 7.5 Summary 261 -- 7.6 Further Reading 262 -- References 262 -- 8 Discrete-time Model Predictive Control (DMPC) of Electrical Drives and Power Converter 265 -- 8.1 Linear Discrete-time Model for PMSM 266 -- 8.1.1 Linear Model for PMSM 266 -- 8.1.2 Discretization of the Continuous-time Model 267 -- 8.2 Discrete-time MPC Design with Constraints 268. 8.2.1 Augmented Model 269 -- 8.2.2 Design without Constraints 270 -- 8.2.3 Formulation of the Constraints 272 -- 8.2.4 On-line Solution for Constrained MPC 272 -- 8.3 Experimental Evaluation of DMPC of PMSM 274 -- 8.3.1 The MPC Parameters 274 -- 8.3.2 Constraints 275 -- 8.3.3 Response to Load Disturbances 275 -- 8.3.4 Response to a Staircase Reference 277 -- 8.3.5 Tuning of the MPC controller 278 -- 8.4 Power Converter Control Using DMPC with Experimental Validation 280 -- 8.5 Summary 281 -- 8.6 Further Reading 282 -- References 283 -- 9 Continuous-time Model Predictive Control (CMPC) of Electrical Drives and PowerConverter 285 -- 9.1 Continuous-time MPC Design 286 -- 9.1.1 Augmented Model 286 -- 9.1.2 Description of the Control Trajectories Using Laguerre Functions 287 -- 9.1.3 Continuous-time Predictive Control without Constraints 289 -- 9.1.4 Tuning of CMPC Control System Using Exponential Data Weighting and Prescribed Degree of Stability 292 -- 9.2 CMPC with Nonlinear Constraints 294 -- 9.2.1 Approximation of Nonlinear Constraint Using Four Linear Constraints 294 -- 9.2.2 Approximation of Nonlinear Constraint Using Sixteen Linear Constraints 294 -- 9.2.3 State Feedback Observer 297 -- 9.3 Simulation and Experimental Evaluation of CMPC of Induction Motor 298 -- 9.3.1 Simulation Results 298 -- 9.3.2 Experimental Results 300 -- 9.4 Continuous-time Model Predictive Control of Power Converter 301 -- 9.4.1 Use of Prescribed Degree of Stability in the Design 302 -- 9.4.2 Experimental Results for Rectification Mode 303 -- 9.4.3 Experimental Results for Regeneration Mode 303 -- 9.4.4 Experimental Results for Disturbance Rejection 304 -- 9.5 Gain Scheduled Predictive Controller 305 -- 9.5.1 The Weighting Parameters 305 -- 9.5.2 Gain Scheduled Predictive Control Law 307 -- 9.6 Experimental Results of Gain Scheduled Predictive Control of Induction Motor 309 -- 9.6.1 The First Set of Experimental Results 309 -- 9.6.2 The Second Set of Experimental Results 311 -- 9.6.3 The Third Set of Experimental Results 312. 9.7 Summary 312 -- 9.8 Further Reading 313 -- References 313 -- 10 MATLAB(R)/Simulink(R) Tutorials on Physical Modeling and Test-bed Setup 315 -- 10.1 Building Embedded Functions for Park-Clarke Transformation 315 -- 10.1.1 Park-Clarke Transformation for Current Measurements 316 -- 10.1.2 Inverse Park-Clarke Transformation for Voltage Actuation 317 -- 10.2 Building Simulation Model for PMSM 318 -- 10.3 Building Simulation Model for Induction Motor 320 -- 10.4 Building Simulation Model for Power Converter 325 -- 10.4.1 Embedded MATLAB Function for Phase Locked Loop (PLL) 325 -- 10.4.2 Physical Simulation Model for Grid Connected Voltage Source Converter 328 -- 10.5 PMSM Experimental Setup 332 -- 10.6 Induction Motor Experimental Setup 334 -- 10.6.1 Controller 334 -- 10.6.2 Power Supply 334 -- 10.6.3 Inverter 335 -- 10.6.4 Mechanical Load 335 -- 10.6.5 Induction Motor and Sensors 335 -- 10.7 Grid Connected Power Converter Experimental Setup 335 -- 10.7.1 Controller 335 -- 10.7.2 Inverter 336 -- 10.7.3 Sensors 336 -- 10.8 Summary 337 -- 10.9 Further Reading 337 -- References 337 -- Index 339. |
| Record Nr. | UNINA-9910819424203321 |
|
Wang Liuping
|
||
| Solaris South Tower, Singapore : , : John Wiley & Sons, Inc., , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
PID control system design and automatic tuning using MATLAB/Simulink / / Liuping Wang
| PID control system design and automatic tuning using MATLAB/Simulink / / Liuping Wang |
| Autore | Wang Liuping |
| Pubbl/distr/stampa | Hoboken, New Jersey, : Wiley-IEEE Press, , 2020 |
| Descrizione fisica | 1 online resource (369 pages) |
| Disciplina | 629.8 |
| Soggetto topico | PID controllers |
| ISBN |
1-119-46937-6
1-119-46940-6 1-119-46941-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xv -- Acknowledgment xvii -- List of Symbols and Acronyms xix -- About the Companion Website xxi -- 1 Basics of PID Control 1 -- 1.1 Introduction 1 -- 1.2 PID Controller Structure 1 -- 1.2.1 Proportional Controller 1 -- 1.2.2 Proportional Plus Derivative Controller 3 -- 1.2.3 Proportional Plus Integral Controller 5 -- 1.2.4 PID Controllers 9 -- 1.2.5 The Commercial PID Controller Structure 12 -- 1.2.6 Food for Thought 13 -- 1.3 Classical Tuning Rules for PID Controllers 13 -- 1.3.1 ZiegleŕôNichols Oscillation Based Tuning Rules 13 -- 1.3.2 Tuning Rules based on the First Order Plus Delay Model 15 -- 1.3.3 Food for Thought 17 -- 1.4 Model Based PID Controller Tuning Rules 18 -- 1.4.1 IMC-PID Controller Tuning Rules 18 -- 1.4.2 Padula and Visioli Tuning Rules 19 -- 1.4.3 Wang and Cluett Tuning Rules 20 -- 1.4.4 Food for Thought 21 -- 1.5 Examples for Evaluations of the Tuning Rules 21 -- 1.5.1 Examples for Evaluating the Tuning Rules 21 -- 1.5.2 Fired Heater Control Example 25 -- 1.6 Summary 27 -- 1.7 Further Reading 28 -- Problems 28 -- 2 Closed-loop Performance and Stability 31 -- 2.1 Introduction 31 -- 2.2 Routh́ôHurwitz Stability Criterion 31 -- 2.2.1 Determining Closed-loop Poles 32 -- 2.2.2 Routh́ôHurwitz Stability Criterion 33 -- 2.2.3 Food for Thought 36 -- 2.3 Nyquist Stability Criterion 36 -- 2.3.1 Nyquist Diagram 36 -- 2.3.1.1 Gain Margin 38 -- 2.3.1.2 Phase Margin 38 -- 2.3.1.3 Delay Margin 38 -- 2.3.2 Rework of Tuning Rules based PID Controllers 40 -- 2.3.3 Food for Thought 42 -- 2.4 Control System Structures and Sensitivity Functions 42 -- 2.4.1 One Degree of Freedom Control System Structure 43 -- 2.4.2 Two Degrees of Freedom Design 44 -- 2.4.2.1 Two degrees of freedom implementation of PI controllers 45 -- 2.4.3 Sensitivity Functions in Feedback Control 45 -- 2.4.4 Food for Thought 47 -- 2.5 Reference Following and Disturbance Rejection 47 -- 2.5.1 Closed-loop Bandwidth 47 -- 2.5.2 Reference Following and Disturbance Rejection with PID Controllers 50.
2.5.3 Reference Following and Disturbance Rejection with Resonant Controllers 53 -- 2.5.4 Food for Thought 54 -- 2.6 Disturbance Rejection and Noise Attenuation 54 -- 2.6.1 Conflict between Disturbance Rejection and Noise Attenuation 54 -- 2.6.2 PID Controller for Disturbance Rejection and Noise Attenuation 55 -- 2.6.3 Food for Thought 58 -- 2.7 Robust Stability and Robust Performance 59 -- 2.7.1 Modeling Errors 59 -- 2.7.2 Robust Stability 60 -- 2.7.3 Case Study: Robust Control of Polymer Reactor 62 -- 2.7.4 Food for Thought 65 -- 2.8 Summary 65 -- 2.9 Further Reading 67 -- Problems 67 -- 3 Model-Based PID and Resonant Controller Design 71 -- 3.1 Introduction 71 -- 3.2 PI Controller Design 71 -- 3.2.1 Desired Closed-loop Performance Specification 71 -- 3.2.2 Model and Controller Structures 72 -- 3.2.3 Closed-loop Transfer Functions for Different Configurations 75 -- 3.2.4 Food for Thought 77 -- 3.3 Model Based Design for PID Controllers 78 -- 3.3.1 PD Controller Design 78 -- 3.3.2 Analytical Examples for Ideal PID with Pole-zero Cancellation 81 -- 3.3.3 Analytical Examples for PID Controllers with Filters 84 -- 3.3.4 PID Controller Design without PoléôZero Cancellation 92 -- 3.3.5 MATLAB Tutorial on Solution of a PID Controller with Filter 94 -- 3.3.6 Food for Thought 95 -- 3.4 Resonant Controller Design 96 -- 3.4.1 Resonant Controller Design 96 -- 3.4.2 Steady-state Error Analysis 97 -- 3.4.3 PoléôZero Cancellation in the Design of a Resonant Controller 99 -- 3.4.4 Food for Thought 101 -- 3.5 Feedforward Control 102 -- 3.5.1 Basic Ideas about Feedforward Control 102 -- 3.5.2 Three Springs and Double Mass System 103 -- 3.5.3 Food for Thought 108 -- 3.6 Summary 108 -- 3.7 Further Reading 108 -- Problems 109 -- 4 Implementation of PID Controllers 113 -- 4.1 Introduction 113 -- 4.2 Scenario of a PID Controller at work 113 -- 4.3 PID Controller Implementation using the Position Form 114 -- 4.3.1 The Steady-state Information Needed 114 -- 4.3.2 Discretization of a PID Controller 115. 4.3.3 Food for Thought 116 -- 4.4 PID Controller Implementation using the Velocity Form 117 -- 4.4.1 Discretization of a PI Controller 117 -- 4.4.2 Discretization of a PID Controller using the Velocity Form 119 -- 4.4.3 Improving Accuracy in a Slower Sampling Environment 120 -- 4.4.4 Food for Thought 122 -- 4.5 Anti-windup Implementation using the Position Form 122 -- 4.5.1 Integrator Windup Scenario 122 -- 4.5.2 Anti-windup Mechanisms in the Position Form of PI Controllers 124 -- 4.5.3 Food for Thought 125 -- 4.6 Anti-windup Mechanisms in the Velocity Form 126 -- 4.6.1 Anti-windup Mechanism on the Amplitude of the Control Signal 126 -- 4.6.2 Limits on the Rate of Change of the Control Signal 129 -- 4.6.3 Food for Thought 129 -- 4.7 Tutorial on PID Anti-windup Implementation 130 -- 4.8 Dealing with Other Implementation Issues 133 -- 4.8.1 Plant Start-up 134 -- 4.8.2 Dealing with Quantization Errors in PID Controller Implementation 135 -- 4.9 Summary 136 -- 4.10 Further Reading 137 -- Problems 137 -- 5 Disturbance Observer- Based PID and Resonant Controller 139 -- 5.1 Introduction 139 -- 5.2 Disturbance observer-Based PI Controller 139 -- 5.2.1 Estimation of Disturbance with Control 139 -- 5.2.1.1 Choice of Proportional Controller K1 140 -- 5.2.1.2 Compensation of Steady-state Error 140 -- 5.2.1.3 The closed-loop poles 141 -- 5.2.1.4 Implementation procedure 142 -- 5.2.2 Equivalence to PI controller 143 -- 5.2.3 MATLAB Tutorial for Implementation of a PI Controller via Estimation 144 -- 5.2.4 Examples for Estimator based PI Controllers 145 -- 5.2.5 Food for Thought 148 -- 5.3 Disturbance observer-Based PID Controller 149 -- 5.3.1 Proportional Plus Derivative Control 149 -- 5.3.2 Adding Integral Action 150 -- 5.3.3 Equivalence to a PID Controller 151 -- 5.3.4 MATLAB Tutorial on the Implementation of a disturbance observer-based PID Controller 153 -- 5.3.5 Examples for Disturbance observer-based PID Controller 155 -- 5.3.6 Food for Thought 156 -- 5.4 Disturbance observer-Based Resonant Controller 156. 5.4.1 Resonant Controller Design 156 -- 5.4.2 Resonant Controller Implementation 158 -- 5.4.3 Equivalence to a Resonant Controller 159 -- 5.4.4 MATLAB Tutorial on Disturbance observer-Based Resonant Controller Implementation 160 -- 5.4.5 Examples for Disturbance observer-Based Resonant Controllers 162 -- 5.4.6 Food for Thought 167 -- 5.5 Multi-frequency Resonant Controller 167 -- 5.5.1 Adding Integral Action to the Resonant Controller 168 -- 5.5.2 Adding More Periodic Components 170 -- 5.5.3 Food for Thought 171 -- 5.6 Summary 172 -- 5.7 Further Reading 172 -- Problems 173 -- 6 PID Control of Nonlinear Systems 179 -- 6.1 Introduction 179 -- 6.2 Linearization of the Nonlinear Model 179 -- 6.2.1 Approximation of a Nonlinear Function 179 -- 6.2.2 Linearization of nonlinear differential equations 181 -- 6.2.3 Case Study: Linearization of the Coupled Tank Model 181 -- 6.2.4 Case Study: Linearization of the Induction Motor Model 184 -- 6.2.5 Food for Thought 186 -- 6.3 Case Study: Ball and Plate Balancing System 187 -- 6.3.1 Dynamics of the Ball and Plate Balancing System 187 -- 6.3.2 Linearization of the Nonlinear Model 188 -- 6.3.3 PID Controller Design 189 -- 6.3.4 Implementation and Experimental Results 190 -- 6.3.4.1 Disturbance Rejection 191 -- 6.3.4.2 Making a Square Movement 192 -- 6.3.4.3 Making a Circle Movement 192 -- 6.3.4.4 Making more Complicated Movements 194 -- 6.3.5 Food for Thought 194 -- 6.4 Gain Scheduled PID Control Systems 194 -- 6.4.1 TheWeighting Parameters 194 -- 6.4.2 Gain Scheduled Implementation using PID Velocity Form 196 -- 6.4.3 Gain Scheduled Implementation using an Estimator Based PID Controller 197 -- 6.4.4 Food for Thought 199 -- 6.5 Summary 199 -- 6.6 Further Reading 199 -- Problems 200 -- 7 Cascade PID Control Systems 203 -- 7.1 Introduction 203 -- 7.2 Design of a Cascade PID Control System 203 -- 7.2.1 Design Steps for a Cascade Control System 203 -- 7.2.2 Simple Design Examples 204 -- 7.2.3 Achieving Closed-loop Performance Invariance (Approximate) in a Cascade Structure 208. 7.2.4 Food for Thought 209 -- 7.3 Cascade Control System for Input Disturbance Rejection 209 -- 7.3.1 Frequency Characteristics for Disturbance Rejection 210 -- 7.3.2 Simulation Studies 211 -- 7.3.3 Food for Thought 213 -- 7.4 Cascade Control System for Actuator Nonlinearities 214 -- 7.4.1 Cascade Control for Actuator with a Deadzone 214 -- 7.4.2 Cascade Control for Actuators with Quantization Errors 218 -- 7.4.3 Cascade Control for Actuators with Backlash Nonlinearity 221 -- 7.4.4 Food for Thought 227 -- 7.5 Summary 230 -- 7.6 Further Reading 230 -- Problems 231 -- 8 PID Controller Design for Complex Systems 233 -- 8.1 Introduction 233 -- 8.2 PI Controller Design via Gain and Phase Margins 233 -- 8.2.1 PI Controller Design Using Gain Margin and Phase Margin Specifications 233 -- 8.2.2 Design Examples 234 -- 8.2.3 Food for Thought 238 -- 8.3 PID Controller Design using Two Frequency Points 238 -- 8.3.1 Finding the PID Controller Parameters 238 -- 8.3.2 Desired Closed-loop Performance Specification using Two Frequency Points 240 -- 8.3.3 Design Examples 242 -- 8.3.4 MATLAB Tutorial on PID Controller Design Using two Frequency Points 243 -- 8.3.5 PID Controller Design for Beer Filtration Process 245 -- 8.3.6 Food for Thought 248 -- 8.4 PID Controller Design for Integrating Systems 249 -- 8.4.1 The Approximate Model 249 -- 8.4.2 Selection of Desired Closed-loop Performance 250 -- 8.4.3 Normalization of the Parameters and Empirical Rules 251 -- 8.4.4 Gain and Phase Margins 253 -- 8.4.5 Simulation Examples 253 -- 8.4.6 Food for Thought 256 -- 8.5 Summary 256 -- 8.6 Further Reading 257 -- Problems 257 -- 9 Automatic Tuning of PID Controllers 259 -- 9.1 Introduction 259 -- 9.2 Relay Feedback Control 259 -- 9.2.1 Relay Control with Hysteresis 259 -- 9.2.2 Relay Control with Integrator 263 -- 9.2.3 Food for Thought 267 -- 9.3 Estimation of Frequency Response using the Fast Fourier Transform (FFT) 267 -- 9.3.1 FFT Estimation 268 -- 9.3.2 MATLAB Tutorial using the FFT for Estimation 269. 9.3.3 Monte-Carlo Simulation Studies 270 -- 9.3.4 Food for Thought 272 -- 9.4 Estimation of Frequency Response Using the frequency sampling filter (FSF) 273 -- 9.4.1 Frequency Sampling FilterModel 273 -- 9.4.2 MATLAB Tutorial on Estimation Using the FSF Model 276 -- 9.4.3 Monte-Carlo Simulation using the FSF Estimation 278 -- 9.4.4 Food for Thought 279 -- 9.5 Monte-Carlo Simulation Studies 279 -- 9.5.1 Effect of Unknown Constant Disturbance 279 -- 9.5.2 Effect of Unknown Low Frequency Disturbance 280 -- 9.5.3 Estimation of the Steady-state Value 282 -- 9.5.4 Food for Thought 283 -- 9.6 Auto-tuner Design for Stable Plant 283 -- 9.6.1 MATLAB Tutorial on Auto-tuner for Stable Plant 284 -- 9.6.2 Evaluation of the Auto-tuner for a Stable Plant 286 -- 9.6.2.1 PID Controller Parameters 287 -- 9.6.2.2 Nyquist Plots 287 -- 9.6.2.3 Closed-loop Simulation Results 288 -- 9.6.3 Comparative Studies 289 -- 9.6.4 Food for Thought 290 -- 9.7 Auto-tuner Design for a Plant with an Integrator 291 -- 9.7.1 Estimation of an Integrating Plus Delay Model 291 -- 9.7.2 Auto-tuner for Integrating Systems 292 -- 9.7.3 Auto-tuning of Cascade Control Systems 297 -- 9.7.4 Food for Thought 300 -- 9.8 Summary 300 -- 9.9 Further Reading 301 -- Problems 302 -- 10 PID Control of Multi-rotor Unmanned Aerial Vehicles 305 -- 10.1 Introduction 305 -- 10.2 Multi-rotor Dynamics 305 -- 10.2.1 Dynamic Models for Attitude Control 305 -- 10.2.2 Actuator Dynamics for Quadrotor UAVs 307 -- 10.2.3 Actuator Dynamics of Hexacopters 309 -- 10.2.4 Food for Thought 311 -- 10.3 Cascade Attitude Control of Multi-rotor UAVs 311 -- 10.3.1 Linearized Model for the Secondary Plant 312 -- 10.3.2 Linearized Model for the Primary Plant 313 -- 10.3.3 Food for Thought 313 -- 10.4 Automatic Tuning of Attitude Control Systems 313 -- 10.4.1 Test Rigs for Auto-tuning Cascade PI Controllers of Multi-rotor UAVs 314 -- 10.4.2 Experimental Results for Quadrotor UAV 314 -- 10.4.3 Experimental Results for Hexacopter 320 -- 10.4.4 Food for Thought 324. 10.5 Summary 324 -- 10.6 Further Reading 325 -- Problems 325 -- Suggestions to Food for Thought Questions 327 -- Bibliography 331 -- Index 341. |
| Record Nr. | UNINA-9910555092903321 |
|
Wang Liuping
|
||
| Hoboken, New Jersey, : Wiley-IEEE Press, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
State feedback control and estimation with MATLAB/Simulink tutorials / / Liuping Wang, Robin Ping Guan
| State feedback control and estimation with MATLAB/Simulink tutorials / / Liuping Wang, Robin Ping Guan |
| Autore | Wang Liuping |
| Pubbl/distr/stampa | England : , : John Wiley & Sons, Incorporated, , [2023] |
| Descrizione fisica | 1 online resource (451 pages) |
| Disciplina | 910.5 |
| Collana | IEEE Press Ser. |
| Soggetto topico | Feedback control systems |
| ISBN |
1-119-69462-0
1-119-69464-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Author Biography -- Preface -- Acknowledgments -- List of Symbols and Acronyms -- About the Companion Website -- Part I Continuous‐time State Feedback Control -- Chapter 1 State Feedback Controller and Observer Design -- 1.1 Introduction -- 1.2 Motivation for Going Beyond PID Control -- 1.3 Basics in State Feedback Control -- 1.3.1 State Feedback Control -- 1.3.2 Controllability -- 1.3.3 Food for Thought -- 1.4 Pole‐assignment Controller -- 1.4.1 The Design Method -- 1.4.2 Similarity Transformation for Controller Design -- 1.4.3 MATLAB Tutorial on Pole‐assignment Controller -- 1.4.4 Food for Thought -- 1.5 Linear Quadratic Regulator (LQR) Design -- 1.5.1 Motivational Example -- 1.5.2 Linear Quadratic Regulator Design -- 1.5.3 Selection of Q and R Matrices -- 1.5.4 LQR with Prescribed Degree of Stability -- 1.5.5 Food for Thought -- 1.6 Observer Design -- 1.6.1 Motivational Example for Observer -- 1.6.2 Observer Design -- 1.6.3 Observability -- 1.6.4 Duality between Controller and Observer -- 1.6.5 Observer Implementation -- 1.6.6 Food for Thought -- 1.7 State Estimate Feedback Control System -- 1.7.1 State Estimate Feedback Control -- 1.7.2 Separation Principle -- 1.7.3 Food for Thought -- 1.8 Summary -- 1.9 Further Reading -- Problems -- Chapter 2 Practical Multivariable Controllers in Continuous‐time -- 2.1 Introduction -- 2.2 Practical Controller I: Integral Action via Controller Design -- 2.2.1 The Original Control Law -- 2.2.2 Integrator Windup Scenarios -- 2.2.3 Proposed Practical Multivariable Controller -- 2.2.4 Anti‐windup Implementation -- 2.2.5 MATLAB Tutorial on Design and Implementation -- 2.2.6 Application to Drum Boiler Control -- 2.2.7 Food for Thought -- 2.3 Practical Controller II: Integral Action via Observer Design -- 2.3.1 Integral Control via Disturbance Estimation.
2.3.2 Anti‐windup Mechanism -- 2.3.3 MATLAB Tutorial on Design and Implementation -- 2.3.4 Application to Sugar Mill Control -- 2.3.5 Design for Systems with Known States -- 2.3.6 Food for Thought -- 2.4 Drive Train Control of a Wind Turbine -- 2.4.1 Modelling of Wind Turbine's Drive Train -- 2.4.2 Configuration of The Control System -- 2.4.3 Design Method I -- 2.4.4 Design Method II -- 2.4.5 MATLAB Tutorial on Design Method II -- 2.4.6 Food for Thought -- 2.5 Summary -- 2.6 Further Reading -- Problems -- Part II Discrete‐time State Feedback Control -- Chapter 3 Introduction to Discrete‐time Systems -- 3.1 Introduction -- 3.2 Discretization of Continuous‐time Models -- 3.2.1 Sampling of a Continuous‐time Model -- 3.2.2 Stability of Discrete‐time System -- 3.2.3 Examples of Discrete‐time Models from Sampling -- 3.2.4 Food for Thoughts -- 3.3 Input and Output Discrete‐time Models -- 3.3.1 Input and Output Models -- 3.3.2 Finite Impulse Response and Step Response Models -- 3.3.3 Non‐minimal State Space Realization -- 3.3.4 Food for Thought -- 3.4 z‐Transforms -- 3.4.1 z‐Transforms for Commonly Used Signals -- 3.4.2 z‐Transfer Functions -- 3.4.3 Food for Thought -- 3.5 Summary -- 3.6 Further Reading -- Problems -- Chapter 4 Discrete‐time State Feedback Control -- 4.1 Introduction -- 4.2 Discrete‐time State Feedback Control -- 4.2.1 Basic Ideas -- 4.2.2 Controllability in Discrete‐time -- 4.2.3 Food for Thought -- 4.3 Discrete‐time Observer Design -- 4.3.1 Basic Ideas about Discrete‐time Observer -- 4.3.2 Observability in Discrete‐time -- 4.3.3 Food for Thought -- 4.4 Discrete‐time Linear Quadratic Regulator (DLQR) -- 4.4.1 Objective Function for DLQR -- 4.4.2 Optimal Solution -- 4.4.3 Observer Design using DLQR -- 4.4.4 Food for Thought -- 4.5 Discrete‐time LQR with Prescribed Degree of Stability. 4.5.1 Basic Ideas about a Prescribed Degree of Stability -- 4.5.2 Case Studies -- 4.5.3 Food for Thought -- 4.6 Summary -- 4.7 Further Reading -- Problems -- Chapter 5 Disturbance Rejection and Reference Tracking via Observer Design -- 5.1 Introduction -- 5.2 Disturbance Models -- 5.2.1 Commonly Encountered Disturbance Signals -- 5.2.2 State Space Model with Input Disturbance -- 5.2.3 Food for Thought -- 5.3 Compensation of Input and Output Disturbances in Estimation -- 5.3.1 Motivational Example -- 5.3.2 Input Disturbance Observer Design -- 5.3.3 MATLAB Tutorial for Augmented State Space Model -- 5.3.4 The Observer Error System -- 5.3.5 Output Disturbance Observer Design -- 5.3.6 Food for Thought -- 5.4 Disturbance‐Observer‐based State Feedback Control -- 5.4.1 The Control Law -- 5.4.2 MATLAB Tutorial for Control Implementation -- 5.4.3 Food for Thought -- 5.5 Analysis of Disturbance‐Observer‐based Control System -- 5.5.1 Controller Transfer Function -- 5.5.2 Disturbance Rejection -- 5.5.3 Reference Tracking -- 5.5.4 A Case Study -- 5.5.5 Food for Thought -- 5.6 Anti‐windup Implementation of the Control Law -- 5.6.1 Algorithm for Anti‐windup Implementation -- 5.6.2 Heating Furnace Control -- 5.6.3 Example for Bandlimited Disturbance -- 5.6.4 Food for Thought -- 5.7 Summary -- 5.8 Further Reading -- Problems -- Chapter 6 Disturbance Rejection and Reference Tracking via Control Design -- 6.1 Introduction -- 6.2 Embedding Disturbance Model into Controller Design -- 6.2.1 Formulation of Augmented State Space Model -- 6.2.2 MATLAB Tutorial -- 6.2.3 Controllability and Observability -- 6.2.4 Food for Thought -- 6.3 Controller and Observer Design -- 6.3.1 Controller Design and Control Signal Calculation -- 6.3.2 Adding Reference Signal -- 6.3.3 Observer Design and Implementation -- 6.3.4 MATLAB Tutorial for Control Implementation -- 6.3.5 Food for Thought. 6.4 Practical Issues -- 6.4.1 Reducing Overshoot in Reference Tracking -- 6.4.2 Anti‐windup Implementation -- 6.4.3 Control System using NMSS Realization -- 6.4.4 Food for Thought -- 6.5 Repetitive Control -- 6.5.1 Basic Ideas about Repetitive Control -- 6.5.2 Determining the Disturbance Model D(z) -- 6.5.3 Robotic Arm Control -- 6.5.4 Food for Thought -- 6.6 Summary -- 6.7 Further Reading -- Problems -- Part III Kalman Filtering -- Chapter 7 The Kalman Filter -- 7.1 Introduction -- 7.2 The Kalman Filter Algorithm -- 7.2.1 State Space Models in the Kalman Filter -- 7.2.2 An Intuitive Computational Procedure -- 7.2.3 Optimization of Kalman Filter Gain -- 7.2.4 Kalman Filter Examples with MATLAB Tutorials -- 7.2.5 Compensation of Sensor Bias and Load Disturbance -- 7.2.6 Food for Thought -- 7.3 The Kalman Filter in Multi‐rate Sampling Environment -- 7.3.1 KF Algorithm for Missing Data Scenarios -- 7.3.2 Case Studies with MATLAB Tutorial -- 7.3.3 Food for Thought -- 7.4 The Extended Kalman Filter (EKF) -- 7.4.1 Linearization in Extended Kalman Filter -- 7.4.2 The Extended Kalman Filter Algorithm -- 7.4.3 Case Studies with MATLAB Tutorial -- 7.4.4 Food for Thought -- 7.5 The Kalman Filter with Fading Memory -- 7.5.1 The Algorithm for KF with Fading Memory -- 7.5.2 Food for Thought -- 7.6 Relationship between Kalman Filter and Observer -- 7.6.1 One‐step Kalman Filter Algorithm -- 7.6.2 Kalman Filter and Observer -- 7.6.3 Food for Thought -- 7.7 Summary -- 7.8 Further Reading -- Problems -- Chapter 8 Addressing Computational Issues in KF -- 8.1 Introduction -- 8.2 The Sequential Kalman Filter -- 8.2.1 Basic Ideas about Sequential Kalman Filter -- 8.2.2 Non‐diagonal R -- 8.2.3 MATLAB Tutorial for Sequential Kalman Filter -- 8.2.4 Food for Thought -- 8.3 The Kalman Filter using UDUT Factorization -- 8.3.1 Gram‐Schmidt Orthogonalization Procedure. 8.3.2 Basic Ideas -- 8.3.3 Sequential Kalman Filter with UDUT Decomposition -- 8.3.4 MATLAB Tutorial -- 8.3.5 Food for Thought -- 8.4 Summary -- 8.5 Further Reading -- Problems -- Bibliography -- Index -- EULA. |
| Record Nr. | UNINA-9910643273703321 |
|
Wang Liuping
|
||
| England : , : John Wiley & Sons, Incorporated, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
State feedback control and estimation with MATLAB/Simulink tutorials / / Liuping Wang, Robin Ping Guan
| State feedback control and estimation with MATLAB/Simulink tutorials / / Liuping Wang, Robin Ping Guan |
| Autore | Wang Liuping |
| Pubbl/distr/stampa | England : , : John Wiley & Sons, Incorporated, , [2023] |
| Descrizione fisica | 1 online resource (451 pages) |
| Disciplina | 910.5 |
| Collana | IEEE Press |
| Soggetto topico | Feedback control systems |
| ISBN |
1-119-69462-0
1-119-69464-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Author Biography -- Preface -- Acknowledgments -- List of Symbols and Acronyms -- About the Companion Website -- Part I Continuous‐time State Feedback Control -- Chapter 1 State Feedback Controller and Observer Design -- 1.1 Introduction -- 1.2 Motivation for Going Beyond PID Control -- 1.3 Basics in State Feedback Control -- 1.3.1 State Feedback Control -- 1.3.2 Controllability -- 1.3.3 Food for Thought -- 1.4 Pole‐assignment Controller -- 1.4.1 The Design Method -- 1.4.2 Similarity Transformation for Controller Design -- 1.4.3 MATLAB Tutorial on Pole‐assignment Controller -- 1.4.4 Food for Thought -- 1.5 Linear Quadratic Regulator (LQR) Design -- 1.5.1 Motivational Example -- 1.5.2 Linear Quadratic Regulator Design -- 1.5.3 Selection of Q and R Matrices -- 1.5.4 LQR with Prescribed Degree of Stability -- 1.5.5 Food for Thought -- 1.6 Observer Design -- 1.6.1 Motivational Example for Observer -- 1.6.2 Observer Design -- 1.6.3 Observability -- 1.6.4 Duality between Controller and Observer -- 1.6.5 Observer Implementation -- 1.6.6 Food for Thought -- 1.7 State Estimate Feedback Control System -- 1.7.1 State Estimate Feedback Control -- 1.7.2 Separation Principle -- 1.7.3 Food for Thought -- 1.8 Summary -- 1.9 Further Reading -- Problems -- Chapter 2 Practical Multivariable Controllers in Continuous‐time -- 2.1 Introduction -- 2.2 Practical Controller I: Integral Action via Controller Design -- 2.2.1 The Original Control Law -- 2.2.2 Integrator Windup Scenarios -- 2.2.3 Proposed Practical Multivariable Controller -- 2.2.4 Anti‐windup Implementation -- 2.2.5 MATLAB Tutorial on Design and Implementation -- 2.2.6 Application to Drum Boiler Control -- 2.2.7 Food for Thought -- 2.3 Practical Controller II: Integral Action via Observer Design -- 2.3.1 Integral Control via Disturbance Estimation.
2.3.2 Anti‐windup Mechanism -- 2.3.3 MATLAB Tutorial on Design and Implementation -- 2.3.4 Application to Sugar Mill Control -- 2.3.5 Design for Systems with Known States -- 2.3.6 Food for Thought -- 2.4 Drive Train Control of a Wind Turbine -- 2.4.1 Modelling of Wind Turbine's Drive Train -- 2.4.2 Configuration of The Control System -- 2.4.3 Design Method I -- 2.4.4 Design Method II -- 2.4.5 MATLAB Tutorial on Design Method II -- 2.4.6 Food for Thought -- 2.5 Summary -- 2.6 Further Reading -- Problems -- Part II Discrete‐time State Feedback Control -- Chapter 3 Introduction to Discrete‐time Systems -- 3.1 Introduction -- 3.2 Discretization of Continuous‐time Models -- 3.2.1 Sampling of a Continuous‐time Model -- 3.2.2 Stability of Discrete‐time System -- 3.2.3 Examples of Discrete‐time Models from Sampling -- 3.2.4 Food for Thoughts -- 3.3 Input and Output Discrete‐time Models -- 3.3.1 Input and Output Models -- 3.3.2 Finite Impulse Response and Step Response Models -- 3.3.3 Non‐minimal State Space Realization -- 3.3.4 Food for Thought -- 3.4 z‐Transforms -- 3.4.1 z‐Transforms for Commonly Used Signals -- 3.4.2 z‐Transfer Functions -- 3.4.3 Food for Thought -- 3.5 Summary -- 3.6 Further Reading -- Problems -- Chapter 4 Discrete‐time State Feedback Control -- 4.1 Introduction -- 4.2 Discrete‐time State Feedback Control -- 4.2.1 Basic Ideas -- 4.2.2 Controllability in Discrete‐time -- 4.2.3 Food for Thought -- 4.3 Discrete‐time Observer Design -- 4.3.1 Basic Ideas about Discrete‐time Observer -- 4.3.2 Observability in Discrete‐time -- 4.3.3 Food for Thought -- 4.4 Discrete‐time Linear Quadratic Regulator (DLQR) -- 4.4.1 Objective Function for DLQR -- 4.4.2 Optimal Solution -- 4.4.3 Observer Design using DLQR -- 4.4.4 Food for Thought -- 4.5 Discrete‐time LQR with Prescribed Degree of Stability. 4.5.1 Basic Ideas about a Prescribed Degree of Stability -- 4.5.2 Case Studies -- 4.5.3 Food for Thought -- 4.6 Summary -- 4.7 Further Reading -- Problems -- Chapter 5 Disturbance Rejection and Reference Tracking via Observer Design -- 5.1 Introduction -- 5.2 Disturbance Models -- 5.2.1 Commonly Encountered Disturbance Signals -- 5.2.2 State Space Model with Input Disturbance -- 5.2.3 Food for Thought -- 5.3 Compensation of Input and Output Disturbances in Estimation -- 5.3.1 Motivational Example -- 5.3.2 Input Disturbance Observer Design -- 5.3.3 MATLAB Tutorial for Augmented State Space Model -- 5.3.4 The Observer Error System -- 5.3.5 Output Disturbance Observer Design -- 5.3.6 Food for Thought -- 5.4 Disturbance‐Observer‐based State Feedback Control -- 5.4.1 The Control Law -- 5.4.2 MATLAB Tutorial for Control Implementation -- 5.4.3 Food for Thought -- 5.5 Analysis of Disturbance‐Observer‐based Control System -- 5.5.1 Controller Transfer Function -- 5.5.2 Disturbance Rejection -- 5.5.3 Reference Tracking -- 5.5.4 A Case Study -- 5.5.5 Food for Thought -- 5.6 Anti‐windup Implementation of the Control Law -- 5.6.1 Algorithm for Anti‐windup Implementation -- 5.6.2 Heating Furnace Control -- 5.6.3 Example for Bandlimited Disturbance -- 5.6.4 Food for Thought -- 5.7 Summary -- 5.8 Further Reading -- Problems -- Chapter 6 Disturbance Rejection and Reference Tracking via Control Design -- 6.1 Introduction -- 6.2 Embedding Disturbance Model into Controller Design -- 6.2.1 Formulation of Augmented State Space Model -- 6.2.2 MATLAB Tutorial -- 6.2.3 Controllability and Observability -- 6.2.4 Food for Thought -- 6.3 Controller and Observer Design -- 6.3.1 Controller Design and Control Signal Calculation -- 6.3.2 Adding Reference Signal -- 6.3.3 Observer Design and Implementation -- 6.3.4 MATLAB Tutorial for Control Implementation -- 6.3.5 Food for Thought. 6.4 Practical Issues -- 6.4.1 Reducing Overshoot in Reference Tracking -- 6.4.2 Anti‐windup Implementation -- 6.4.3 Control System using NMSS Realization -- 6.4.4 Food for Thought -- 6.5 Repetitive Control -- 6.5.1 Basic Ideas about Repetitive Control -- 6.5.2 Determining the Disturbance Model D(z) -- 6.5.3 Robotic Arm Control -- 6.5.4 Food for Thought -- 6.6 Summary -- 6.7 Further Reading -- Problems -- Part III Kalman Filtering -- Chapter 7 The Kalman Filter -- 7.1 Introduction -- 7.2 The Kalman Filter Algorithm -- 7.2.1 State Space Models in the Kalman Filter -- 7.2.2 An Intuitive Computational Procedure -- 7.2.3 Optimization of Kalman Filter Gain -- 7.2.4 Kalman Filter Examples with MATLAB Tutorials -- 7.2.5 Compensation of Sensor Bias and Load Disturbance -- 7.2.6 Food for Thought -- 7.3 The Kalman Filter in Multi‐rate Sampling Environment -- 7.3.1 KF Algorithm for Missing Data Scenarios -- 7.3.2 Case Studies with MATLAB Tutorial -- 7.3.3 Food for Thought -- 7.4 The Extended Kalman Filter (EKF) -- 7.4.1 Linearization in Extended Kalman Filter -- 7.4.2 The Extended Kalman Filter Algorithm -- 7.4.3 Case Studies with MATLAB Tutorial -- 7.4.4 Food for Thought -- 7.5 The Kalman Filter with Fading Memory -- 7.5.1 The Algorithm for KF with Fading Memory -- 7.5.2 Food for Thought -- 7.6 Relationship between Kalman Filter and Observer -- 7.6.1 One‐step Kalman Filter Algorithm -- 7.6.2 Kalman Filter and Observer -- 7.6.3 Food for Thought -- 7.7 Summary -- 7.8 Further Reading -- Problems -- Chapter 8 Addressing Computational Issues in KF -- 8.1 Introduction -- 8.2 The Sequential Kalman Filter -- 8.2.1 Basic Ideas about Sequential Kalman Filter -- 8.2.2 Non‐diagonal R -- 8.2.3 MATLAB Tutorial for Sequential Kalman Filter -- 8.2.4 Food for Thought -- 8.3 The Kalman Filter using UDUT Factorization -- 8.3.1 Gram‐Schmidt Orthogonalization Procedure. 8.3.2 Basic Ideas -- 8.3.3 Sequential Kalman Filter with UDUT Decomposition -- 8.3.4 MATLAB Tutorial -- 8.3.5 Food for Thought -- 8.4 Summary -- 8.5 Further Reading -- Problems -- Bibliography -- Index -- EULA. |
| Record Nr. | UNINA-9910830107403321 |
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Wang Liuping
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| England : , : John Wiley & Sons, Incorporated, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials
| State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials |
| Autore | Wang Liuping |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (451 pages) |
| Altri autori (Persone) | GuanRobin Ping |
| Collana | IEEE Press Ser. |
| ISBN |
1-119-69462-0
1-119-69464-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Author Biography -- Preface -- Acknowledgments -- List of Symbols and Acronyms -- About the Companion Website -- Part I Continuous‐time State Feedback Control -- Chapter 1 State Feedback Controller and Observer Design -- 1.1 Introduction -- 1.2 Motivation for Going Beyond PID Control -- 1.3 Basics in State Feedback Control -- 1.3.1 State Feedback Control -- 1.3.2 Controllability -- 1.3.3 Food for Thought -- 1.4 Pole‐assignment Controller -- 1.4.1 The Design Method -- 1.4.2 Similarity Transformation for Controller Design -- 1.4.3 MATLAB Tutorial on Pole‐assignment Controller -- 1.4.4 Food for Thought -- 1.5 Linear Quadratic Regulator (LQR) Design -- 1.5.1 Motivational Example -- 1.5.2 Linear Quadratic Regulator Design -- 1.5.3 Selection of Q and R Matrices -- 1.5.4 LQR with Prescribed Degree of Stability -- 1.5.5 Food for Thought -- 1.6 Observer Design -- 1.6.1 Motivational Example for Observer -- 1.6.2 Observer Design -- 1.6.3 Observability -- 1.6.4 Duality between Controller and Observer -- 1.6.5 Observer Implementation -- 1.6.6 Food for Thought -- 1.7 State Estimate Feedback Control System -- 1.7.1 State Estimate Feedback Control -- 1.7.2 Separation Principle -- 1.7.3 Food for Thought -- 1.8 Summary -- 1.9 Further Reading -- Problems -- Chapter 2 Practical Multivariable Controllers in Continuous‐time -- 2.1 Introduction -- 2.2 Practical Controller I: Integral Action via Controller Design -- 2.2.1 The Original Control Law -- 2.2.2 Integrator Windup Scenarios -- 2.2.3 Proposed Practical Multivariable Controller -- 2.2.4 Anti‐windup Implementation -- 2.2.5 MATLAB Tutorial on Design and Implementation -- 2.2.6 Application to Drum Boiler Control -- 2.2.7 Food for Thought -- 2.3 Practical Controller II: Integral Action via Observer Design -- 2.3.1 Integral Control via Disturbance Estimation.
2.3.2 Anti‐windup Mechanism -- 2.3.3 MATLAB Tutorial on Design and Implementation -- 2.3.4 Application to Sugar Mill Control -- 2.3.5 Design for Systems with Known States -- 2.3.6 Food for Thought -- 2.4 Drive Train Control of a Wind Turbine -- 2.4.1 Modelling of Wind Turbine's Drive Train -- 2.4.2 Configuration of The Control System -- 2.4.3 Design Method I -- 2.4.4 Design Method II -- 2.4.5 MATLAB Tutorial on Design Method II -- 2.4.6 Food for Thought -- 2.5 Summary -- 2.6 Further Reading -- Problems -- Part II Discrete‐time State Feedback Control -- Chapter 3 Introduction to Discrete‐time Systems -- 3.1 Introduction -- 3.2 Discretization of Continuous‐time Models -- 3.2.1 Sampling of a Continuous‐time Model -- 3.2.2 Stability of Discrete‐time System -- 3.2.3 Examples of Discrete‐time Models from Sampling -- 3.2.4 Food for Thoughts -- 3.3 Input and Output Discrete‐time Models -- 3.3.1 Input and Output Models -- 3.3.2 Finite Impulse Response and Step Response Models -- 3.3.3 Non‐minimal State Space Realization -- 3.3.4 Food for Thought -- 3.4 z‐Transforms -- 3.4.1 z‐Transforms for Commonly Used Signals -- 3.4.2 z‐Transfer Functions -- 3.4.3 Food for Thought -- 3.5 Summary -- 3.6 Further Reading -- Problems -- Chapter 4 Discrete‐time State Feedback Control -- 4.1 Introduction -- 4.2 Discrete‐time State Feedback Control -- 4.2.1 Basic Ideas -- 4.2.2 Controllability in Discrete‐time -- 4.2.3 Food for Thought -- 4.3 Discrete‐time Observer Design -- 4.3.1 Basic Ideas about Discrete‐time Observer -- 4.3.2 Observability in Discrete‐time -- 4.3.3 Food for Thought -- 4.4 Discrete‐time Linear Quadratic Regulator (DLQR) -- 4.4.1 Objective Function for DLQR -- 4.4.2 Optimal Solution -- 4.4.3 Observer Design using DLQR -- 4.4.4 Food for Thought -- 4.5 Discrete‐time LQR with Prescribed Degree of Stability. 4.5.1 Basic Ideas about a Prescribed Degree of Stability -- 4.5.2 Case Studies -- 4.5.3 Food for Thought -- 4.6 Summary -- 4.7 Further Reading -- Problems -- Chapter 5 Disturbance Rejection and Reference Tracking via Observer Design -- 5.1 Introduction -- 5.2 Disturbance Models -- 5.2.1 Commonly Encountered Disturbance Signals -- 5.2.2 State Space Model with Input Disturbance -- 5.2.3 Food for Thought -- 5.3 Compensation of Input and Output Disturbances in Estimation -- 5.3.1 Motivational Example -- 5.3.2 Input Disturbance Observer Design -- 5.3.3 MATLAB Tutorial for Augmented State Space Model -- 5.3.4 The Observer Error System -- 5.3.5 Output Disturbance Observer Design -- 5.3.6 Food for Thought -- 5.4 Disturbance‐Observer‐based State Feedback Control -- 5.4.1 The Control Law -- 5.4.2 MATLAB Tutorial for Control Implementation -- 5.4.3 Food for Thought -- 5.5 Analysis of Disturbance‐Observer‐based Control System -- 5.5.1 Controller Transfer Function -- 5.5.2 Disturbance Rejection -- 5.5.3 Reference Tracking -- 5.5.4 A Case Study -- 5.5.5 Food for Thought -- 5.6 Anti‐windup Implementation of the Control Law -- 5.6.1 Algorithm for Anti‐windup Implementation -- 5.6.2 Heating Furnace Control -- 5.6.3 Example for Bandlimited Disturbance -- 5.6.4 Food for Thought -- 5.7 Summary -- 5.8 Further Reading -- Problems -- Chapter 6 Disturbance Rejection and Reference Tracking via Control Design -- 6.1 Introduction -- 6.2 Embedding Disturbance Model into Controller Design -- 6.2.1 Formulation of Augmented State Space Model -- 6.2.2 MATLAB Tutorial -- 6.2.3 Controllability and Observability -- 6.2.4 Food for Thought -- 6.3 Controller and Observer Design -- 6.3.1 Controller Design and Control Signal Calculation -- 6.3.2 Adding Reference Signal -- 6.3.3 Observer Design and Implementation -- 6.3.4 MATLAB Tutorial for Control Implementation -- 6.3.5 Food for Thought. 6.4 Practical Issues -- 6.4.1 Reducing Overshoot in Reference Tracking -- 6.4.2 Anti‐windup Implementation -- 6.4.3 Control System using NMSS Realization -- 6.4.4 Food for Thought -- 6.5 Repetitive Control -- 6.5.1 Basic Ideas about Repetitive Control -- 6.5.2 Determining the Disturbance Model D(z) -- 6.5.3 Robotic Arm Control -- 6.5.4 Food for Thought -- 6.6 Summary -- 6.7 Further Reading -- Problems -- Part III Kalman Filtering -- Chapter 7 The Kalman Filter -- 7.1 Introduction -- 7.2 The Kalman Filter Algorithm -- 7.2.1 State Space Models in the Kalman Filter -- 7.2.2 An Intuitive Computational Procedure -- 7.2.3 Optimization of Kalman Filter Gain -- 7.2.4 Kalman Filter Examples with MATLAB Tutorials -- 7.2.5 Compensation of Sensor Bias and Load Disturbance -- 7.2.6 Food for Thought -- 7.3 The Kalman Filter in Multi‐rate Sampling Environment -- 7.3.1 KF Algorithm for Missing Data Scenarios -- 7.3.2 Case Studies with MATLAB Tutorial -- 7.3.3 Food for Thought -- 7.4 The Extended Kalman Filter (EKF) -- 7.4.1 Linearization in Extended Kalman Filter -- 7.4.2 The Extended Kalman Filter Algorithm -- 7.4.3 Case Studies with MATLAB Tutorial -- 7.4.4 Food for Thought -- 7.5 The Kalman Filter with Fading Memory -- 7.5.1 The Algorithm for KF with Fading Memory -- 7.5.2 Food for Thought -- 7.6 Relationship between Kalman Filter and Observer -- 7.6.1 One‐step Kalman Filter Algorithm -- 7.6.2 Kalman Filter and Observer -- 7.6.3 Food for Thought -- 7.7 Summary -- 7.8 Further Reading -- Problems -- Chapter 8 Addressing Computational Issues in KF -- 8.1 Introduction -- 8.2 The Sequential Kalman Filter -- 8.2.1 Basic Ideas about Sequential Kalman Filter -- 8.2.2 Non‐diagonal R -- 8.2.3 MATLAB Tutorial for Sequential Kalman Filter -- 8.2.4 Food for Thought -- 8.3 The Kalman Filter using UDUT Factorization -- 8.3.1 Gram‐Schmidt Orthogonalization Procedure. 8.3.2 Basic Ideas -- 8.3.3 Sequential Kalman Filter with UDUT Decomposition -- 8.3.4 MATLAB Tutorial -- 8.3.5 Food for Thought -- 8.4 Summary -- 8.5 Further Reading -- Problems -- Bibliography -- Index -- EULA. |
| Record Nr. | UNINA-9910623984103321 |
|
Wang Liuping
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
