Modelling and Control for Intelligent Industrial Systems (eBook)

Title Page 1
Foreword 6
Preface 7
Contents 17
Acronyms 25
Industrial Robots in Contact-Free Operation 26
Dynamic Analysis of Rigid Link Robots 26
Kinematic Analysis of Rigid Link Robots 30
Dynamic Analysis of Flexible-Link Robots 33
Kinematic Analysis of Flexible-Link Robots 34
Control of Rigid-Link Robots in Contact-Free Operation 38
Control of Flexible-Link Robots in Contact-Free Operation 39
Inverse Dynamics Control of Flexible-Link Robots 39
Energy-Based Control of Flexible Link Robots 41
Adaptive Neural Control of Flexible Manipulators 44
Approximation of the Flexible-Links Dynamics 47
Simulation of Flexible-Link Robot Control 50
Model-Based Control of Flexible-Link Robots 50
Energy-Based Control 51
Adaptive Neural Control 53
Industrial Robots in Compliance Tasks 56
Impedance Control 56
Hybrid Position/Force Control 59
Stiffness Identification in Compliance Tasks 60
Application of Robot Hybrid Position/Force Control 62
Force Control of Flexible-Link Robots 64
Interaction with the Compliant Surface 64
Force Control for Flexible-Link Robots 65
Simulation of Force Control for Flexible-Link Robots 66
Mobile Robots and Autonomous Vehicles 69
Kinematic Analysis of Mobile Robots 69
Control of Autonomous Ground Vehicles 70
Differential Flatness for Finite Dimensional Systems 71
Flatness-Based Control of the Autonomous Vehicle 72
Kinematic and Dynamic Models of Surface Vessels 75
A Generic Kinematic and Dynamic Ship Model 75
Models of Current, Wind and Wave Forces 77
Ship Model for the Dynamic Positioning Problem 78
Ship Actuator Model 78
Feedback Linearization for Ship Dynamic Positioning 79
Ship Control Using Dynamic Feedback Linearization 79
Estimation of the Unknown Additive Disturbances 80
Backstepping Control for the Ship Steering Problem 81
The Ship Steering Problem 81
Nonlinear Backstepping 83
Automated Ship Steering Using Backstepping Control 84
Calculation of the SISO Backstepping Nonlinear Controller 85
Adaptive Control Methods for Industrial Systems 88
Adaptive Control of Industrial Systems with Full State Feedback 88
Problem Statement 88
Transformation to a Regulation Problem 90
Approximators of Unknown System Dynamics 91
Lyapunov Stability Analysis in the Case of Full State Feedback 92
Adaptive Control of Industrial Systems with Output Feedback 94
Transformation to a Regulation Problem 94
Approximation of Unknown System Dynamics 95
Lyapunov Stability Analysis in the Case of Output Feedback 97
Riccati Equation Coefficients and H Control Robustness 99
Application to the Control of Electric Motors 100
The DC Motor Model 100
State Feedback Controller of the DC Motor Model 102
State Feedback Controller for the DC Motor 104
Output Feedback Controller for the DC Motor 108
Application to the Field-Oriented Induction Motor 112
Application to the Ship Steering Control Problem 116
Application to the Stabilization of Electromechanical Systems 119
Robust Control Methods for Industrial Systems 124
Robust Control with Sliding-Mode Control Theory 124
Sliding-Mode Control 124
An Application Example of Sliding-Mode Control 127
Sliding-Mode Control with Boundary Layer 128
Robust Control with Interval Polynomials Theory 130
Basics of Kharitonov's Theory 130
Extremal Properties of Kharitonov Polynomials 132
Application to the Stabilization of Electric Power Systems 133
The Problem of Power System Stabilization 134
Transfer Function of the Single-Machine Infinite-Bus Model 136
Kharitonov's Theory for Power System Stabilization 136
Filtering and Estimation Methods for Industrial Systems 142
Linear State Observers 142
The Continuous-Time Kalman Filter for Linear Models 143
The Discrete-Time Kalman Filter for Linear Systems 144
The Extended Kalman Filter for Nonlinear Systems 145
Sigma-Point Kalman Filters 147
Particle Filters 150
The Particle Approximation of Probability Distributions 150
The Prediction Stage 151
The Correction Stage 151
The Resampling Stage 153
Approaches to the Implementation of Resampling 153
Application of Estimation Methods to Industrial Systems Control 156
Kalman Filter-Based Control of Electric Motors 156
Extended Kalman Filter-Based Control of Electric Motors 157
Unscented Kalman Filter-Based Control of Electric Motors 160
Particle Filter-Based Control of Electric Motors 161
Sensor Fusion-Based Control for Industrial Systems 164
Sensor Fusion-Based Control of Industrial Robots 164
The Sensor Fusion Problem 164
Application of EKF and PF for Sensor Fusion 166
Simulation of EKF and PF-Based Sensor Fusion for Industrial Robot Control 168
Sensor Fusion-Based Control for Mobile Robots 178
Simulation of EKF-Based Control for Mobile Robots 178
Simulation of Particle Filter-Based Mobile Robot Control 184
Simulation of EKF and PF-Based Parallel Parking Control 185
Performance Analysis of EKF and PF-Based Mobile Robot Control 186
Sensor Fusion-Based Dynamic Ship Positioning 188
EKF and PF-Based Sensor Fusion for the Ship Model 188
Simulation of EKF and PF-Based Ship Dynamic Positioning 191
Distributed Filtering and Estimation for Industrial Systems 197
The Problem of Distributed State Estimation over Sensor Networks 197
Distributed Extended Kalman Filtering 199
Calculation of Local Extended Kalman Filter Estimations 199
Extended Information Filtering for State Estimates Fusion 202
Distributed Sigma-Point Kalman Filtering 203
Calculation of Local Unscented Kalman Filter Estimations 203
Unscented Information Filtering for State Estimates Fusion 207
Distributed Particle Filter 208
Distributed Particle Filtering for State Estimation Fusion 208
Fusion of the Local Probability Density Functions 210
Simulation Tests 212
Multi-UAV Control with Extended Information Filtering 212
Multi-UAV Control with Distributed Particle Filtering 216
Fault Detection and Isolation for Industrial Systems 219
Fault Diagnosis with Statistical Methods 219
Residual Generation through Nonlinear System Modelling 219
Determination of the Nonlinear Model's Structure 221
Stages of Nonlinear Systems Modeling 224
Fault Threshold Selection with the Generalized Likelihood Ratio 225
The Local Statistical Approach to Fault Diagnosis 225
Fault Detection with the Local Statistical Approach 226
Fault Isolation with the Local Statistical Approach 228
Fault Threshold for Residuals of Unknown Distribution 230
Application of Fault Diagnosis to Industrial Systems 234
Fault Diagnosis of the Electric Power System 234
Cascading Events in the Electric Power Grid 234
Electric Power Systems Dynamics 237
The Multi-area Multi-machine Electric Power System 238
Nonlinear Modeling of the Electric Power System 240
Fault Diagnosis Tests for the Electric Power System 242
Parameters of the Nonlinear Power System Model 242
Efficiency of the Fault Diagnosis Method 243
Fault Diagnosis of Electric Motors 245
Failures in Rotating Electrical Machines 245
Faults in the DC Motor Control Loop 246
Residual Generation with the Use of Kalman Filtering 246
Residual Generation with the Use of Particle Filtering 247
Fault Diagnosis in Control Loops 249
Optimization Methods for Motion Planning of Multi-robot Systems 252
Distributed Gradient for Motion Planning of Multi-robot Systems 252
Approaches to Multi-robot Motion Planning 252
The Distributed Gradient Algorithm 254
Kinematic Model of the Multi-robot System 254
Cohesion of the Multi-robot System 256
Convergence to the Goal Position 258
Stability Analysis Using La Salle's Theorem 258
Particle Swarm Theory for Multi-robot Motion Planning 260
The Particle Swarm Theory 260
Stability of the Particle Swarm Algorithm 261
Evaluation Tests for the Stochastic Search Algorithms 263
Convergence towards the Equilibrium 263
Tuning of the Stochastic Search Algorithms 270
Optimization Methods for Target Tracking by Multi-robot Systems 273
Distributed Motion Planning and Filtering in Multi-robot Systems 273
Target Tracking in Mobile Sensors Networks 273
The Problem of Distributed Target Tracking 275
Tracking of the Reference Path by the Target 277
Convergence of the Multi-robot System to the Target 278
Simulation Tests 279
Target Tracking Using Extended Information Filtering 279
Target Tracking Using Unscented Information Filtering 282
Optimization Methods for Industrial Automation 288
Multi-objective Optimization for Industrial Automation 288
The Warehouse Replenishment Problem 288
Multi-objective Optimization Problems 289
The Pareto-optimality Principles 289
Replenishment as a Pareto Optimization Problem 291
Approaches to Obtain Pareto-optimal Solutions 291
Graphical Representation of Pareto Optimal Solution 294
Genetic Algorithms in the Search of Pareto-optimal Solutions 294
Basic Principles of Evolutionary Algorithms 294
Evolutionary Algorithms for Multi-objective Optimization 295
Control of Diversity of the Pareto-optimal Solutions 296
Genetic Algorithm Convergence to Pareto-optimal Solutions 299
A Genetic Algorithm for the Warehouse Replenishment Task 299
Constraints of Genetic Algorithms in Ordering Problems 299
The Genetic Algorithm for Replenishment Optimization 301
Mating Procedure 301
Mutation Procedure 303
Definition and Tuning of the Cost Function 304
Results on Genetic Algorithm-Based Warehouse Optimization 305
Cost Function Tuning through Weights Selection 305
Evaluation of the Genetic Algorithm Performance 310
Machine Learning Methods for Industrial Systems Control 312
Model-Free Control of Flexible-Link Robots 312
Approaches for Model-Based Control of Flexible-Link Robots 312
Approaches for Model-Free Control of Flexible-Link Robots 314
Neural Control Using Multi-frequency Basis Functions 315
Neural Control Using Wavelet Basis Functions 316
Wavelet Frames 316
Dyadic Grid Scaling and Orthonormal Wavelet Transforms 317
The Scaling Function and the Multi-resolution Representation 318
Examples of Orthonormal Wavelets 319
The Haar Wavelet 320
Neural Networks Using Hermite Activation Functions 321
Identification with Feed-Forward Neural Networks 321
The Gauss-Hermite Series Expansion 323
Neural Networks Using 2D Hermite Activation Functions 325
Results on Flexible-Link Control and Vibrations Suppression 327
The Flexible-Link Robot Model 327
Control Using Hermite Polynomial-Based Neural Networks 327
Machine Learning Methods for Industrial Systems Fault Diagnosis 331
Automata in Fault Diagnosis Tasks 331
Fault Diagnosis of Systems with Event-Driven Dynamics 331
System Modelling with the Use of Finite Automata 332
System Modelling with the Use of Fuzzy Automata 334
Monitoring Signals with the Use of Fuzzy Automata 335
A Fault Diagnosis Approach Based on Fuzzy Automata 336
Generation of the Templates String 336
Syntactic Analysis Using Fuzzy Automata 337
Detection of Fault Patterns by the Fuzzy Automata 340
Simulation Tests of Fault Diagnosis with Fuzzy Automata 342
Applications of Machine Vision to Industrial Systems 345
Machine Vision and Imaging Transformations 345
Some Basic Transformations 345
Perspective Transformation 347
Camera Model 352
Camera Calibration 354
Stereo Imaging 355
Multi Cameras-Based Visual Servoing for Industrial Robots 357
Distributed Filtering for Sensorless Control 358
Visual Servoing over a Network of Synchronized Cameras 358
Robot's State Estimation through Distributed Filtering 359
Distributed State Estimation Using the EIF 360
Local State Estimation with Extended Kalman Filtering 360
State Estimation through a Nonlinear Transformation 362
Derivative-Free Kalman Filtering for Nonlinear Systems 363
Fusing Estimations from Local Distributed Filters 363
Calculation of the Aggregate State Estimation 365
Simulation Tests of the Vision-Based Control System 365
Dynamics and Control of the Robotic Manipulator 365
Evaluation of Results on Vision-Based Control 366
References 368
References 368
Index 393