Control and Systems Engineering (eBook)

Forewords 1
Preface 1
Contents 1
Reflection on Four Decades of Contributions of My Graduate Students 25
1Introduction 25
2Graduate Students 26
2.1MS Students 26
2.2PhD Students 31
2.3Visiting Overseas Ph.D. Students 36
3Conclusions 36
Proportional-Integral Observer in Robust Control, Fault Detection, and Decentralized Control of Dynamic Systems 37
1Introduction 37
2Observer Fundamentals 38
2.1Full-Order Observer and State Feedback 39
2.2Reduced-Order Observer 40
2.3Functional Observer 42
3Robustness with Observers 46
3.1P-Observer and Loop Transfer Recovery 46
3.2PI-Observer and Robustness 47
4Disturbance Estimation and Fault Detection with Observers 50
4.1Disturbance Observer (DO) 50
4.2Unknown Input Observer (UIO) 51
4.3Proportional-Integral Observer (PIO) 52
4.4Fault Detection 54
5Decentralized PI-Observer Design 55
6Decentralized PI-Observer-Based Control Design 60
6.1bserver Case 61
6.2PI-Observer Case 62
7Conclusion 64
New Application of an Adaptive Controller Based on Robust Fixed Point Transformations 68
1Introduction 68
2Nonlinear Order Reduction in Adaptive Control Based on Approximate Dynamic Model 70
3Immediate Antecedents in WMR Control 71
4The More General Model 73
4.1The Physical Basis 73
4.2The Sign Conventions and the Final Results 74
4.3Combination of the Dynamic Models of the DC Motor and the Cart 76
4.4Anisotropic and Dynamically Varying Resolution of the Non-Holonomic Constraints 77
5The RFPT-Based Adaptivity 79
6Simulation Results 80
7Conclusions 86
Hybrid Functions Approach for Variational Problems and Optimal Control of Delay Systems 90
1Introduction 90
2Properties of Hybrid Functions 93
2.1Hybrid of Block-Pulse and Bernoulli Polynomials 93
2.2Function Approximation 94
2.3Integration of B(t)BT(t) 95
2.4Operational Matrix of Integration 96
2.5The Operational Matrix of Product 96
2.6The Operational Matrix of Delay 97
3The Numerical Methods 97
3.1Problem (a) 97
3.2Problem (b) 99
4Main Feature of the Method 102
5Illustrative Examples 102
5.1Example 1 102
5.2Example 2 104
5.3Example 3 105
5.4Example 4 106
5.5Example 5 107
5.6Example 6 108
6Conclusion 110
Punctuated Anytime Learningfor Autonomous Agent Control 112
1 Introduction 112
2 Punctuated Anytime Learning 113
2.1 The Co-Evolution of Model Parameters 115
2.2 Fitness Biasing 115
3 Using the Co-Evolution of Model Parameters for HexapodRobot Gait Development 116
3.1 Robot and Model 117
3.2 Cyclic Genetic Algorithm 118
3.3 Using the Co-Evolution of Model Parameters to Learn Gaits 118
3.4 Results 120
4 Using Fitness Biasing for Xpilot Game Agent Control 122
4.1 Xpilot-AI 124
4.2 Fitness Biasing Applied to Xpilot-AI 125
4.3 Results 126
5 Discussion 127
6 Conclusion 128
References 129
Big Data Analytic: Cases for Communications SystemsModeling and Renewable Energy Forecast 131
1 Cost-Efficient Approach to ROF Communications SystemsDesign for CATV Channels over WDM Network and Fuzzy-GA Estimation 132
1.1 Introduction 132
1.2 Optimized System Design to Overcome DM Laser Limitations 133
1.3 System Analysis 133
1.4 Simulation Results 134
1.5 Fuzzy-GA Estimation 136
2 Big Data Analytic for 24-hour Ahead Solar Energy Forecast 140
2.1 Introduction 140
2.2 Problem Statement and Solar Forecasting State-of-the-art 141
2.3 Framework Development Proposed Approach to Solar EnergyForecasting
2.4 Performance Evaluation Metrics 150
2.5 Forecast Results and Discussion 151
3 Conclusion and Remarks 153
References 154
Area Coverage in a Fixed-Obstacle Environment Using Mobile Sensor Networks 157
1Introduction 157
2Visibility-Aware Multiplicatively Weighted Voronoi Diagram 159
3Deployment Protocols 161
3.1Obstructed Farthest Point (OFP) Strategy 163
3.2Obstructed Minmax Point (OMP) Strategy 165
4Simulation Results 167
5Conclusions and Future Works 171
Energy Aware Load Prediction for Cloud Data Centers 174
1 Introduction 174
1.1 Related Research 175
1.2 Optimum Cloud Provisioning Method 176
1.3 Research Contributions and Paper Outline 177
2 Stochastic State Change Model 178
2.1 Determination of Probability Distribution Function 179
2.2 Definition of the SLA Cost Function 181
3 Dynamic Quantization Model 182
3.1 Determination of Cost Functions 182
3.2 Prediction Frequency Calculation 183
4 Linear Prediction Model 184
5 Simulation and Results 187
6 Conclusion 193
References 194
Behaving Nicely in a System of Systems –Harmonising with the Environment 196
1 Introduction 196
2 Systems of Systems Engineering 197
2.1 Definition and Characterization of Systems of Systems 197
2.2 Types of System of Systems 198
2.3 Interoperability 200
2.4 Summary of SoS Basics 200
3 Global Drivers for Improved SoS Design and Management 201
3.1 Increasing Interconnectivity, Increasing Complexity, and IncreasedInflexibility 201
3.2 A Perfect Storm for Europe 201
3.3 Agility 203
4 The SoS Research Agenda for Europe 204
4.1 Characterization and Description of SoS 205
4.2 Theoretical Foundations 206
4.3 Emergence 206
4.4 Multi-level Modelling 206
4.5 Measurement and Metrics 207
4.6 Evaluation of SoS 207
4.7 Definition and Evolution of SoS Architecture 208
4.8 Prototyping SoS 208
4.9 Trade-Off 209
4.10 Security 209
4.11 Human Aspects 209
4.12 Energy 210
5 Conclusions 210
References 211
Design Considerations of Dexterous Telerobotics 214
1 Introduction 214
2 Local Site Design Considerations and Challenges 215
2.1 Sensing (Action Identification). 215
2.2 Sensation Generation (Feedback) 217
2 Local Site Design Considerations and Challenges 215
2.1 Sensing (Action Identification). 215
2.2 Sensation Generation (Feedback) 217
3 Remote Site Design Considerations and Challenges 219
3.1 Action Generation 219
3.2 Sensing 221
4 Conclusions 221
References 222
Real-Time Neural Control of Mobile Robots 225
1Introduction 225
2Decentralized Systems 226
3Nonlinear System Identification 228
3.1Neural Identification 228
3.2The EKF Training Algorithm 230
4Inverse Optimal Control 231
5Shrimp Robot Application 234
6Vision Systems Application 238
8.1Stereo Vision 239
8.2Kinematic Planer 242
7 Results 242
8Conclusions 246
Low-Cost Inertial Navigation 250
1Notation 250
2Inertial Navigation 250
2.1Navigation Coordinate Frames 251
2.2Inertial Sensor Technology 252
2.3Inertial Sensor Errors 254
3Inertial Sensor Performance Comparison 258
3.1 Description of an ISA, IMU, IRU, and INS 259
3.2INS-Only Based Navigation 260
3.3An INS–Only Simulation Example 262
3.4INS Error Modeling 266
4GPS-Only Based Navigation 266
5GPS/INS–Based Navigation 268
5.1Introduction 268
5.2Uncoupled GPS/INS Integration 269
5.3Loosely-Coupled GPS/INS Integration 269
5.4Tightly-Coupled GPS/INS Integration 273
5.5Deeply-Coupled GPS/INS 273
6Conclusion 277
Hardware Implementation of Fuzzy Logic Controller-Designability, Stability and Robustness- 279
1 Introduction 279
2 Practical Hardware Systems 280
2.1 Current Mode Integrated Circuits1,2,3,4,5,6,7,8,9,10,11,12,13,14 280
2.2 Membership Function Generator in Voltage Mode15 281
2.3 Fuzzy Inference Engine Board of Voltage Mode Circuit16 282
2.4 Commercial Hardware for Fuzzy Logic Control in Voltage Mode17,18,19 283
2.5 Wine Glass/Mouse Stabilization by Employing the Fuzzy LogicController20 286
2.6 Fuzzy Chips –Rule Chip and Defuzzifier Chip– 288
3 Applications 289
4 Conclusions 290
References 290
Decision Making under Z-Information 292
1 Critical Analysis of the Existing Decision Theories 292
2 Isights for Development of New Theory of Decisions 294
3 The Evolut?on of Model?ng Dec?s?on-Relevant Informat?on 294
4 Basic Principles of the Suggested New Theory of Decisions 295
4.1 Z-Restriction 295
4.2 Combined States 295
4.3 Short Description of a New Theory 296
5 Statement of the Problem in the Suggested Theory ofDecisions 297
6 Model 299
7 Solving Methodology for a Suggested Theory of Decisions 303
8 Conclusion 304
References 304
Agencies of Intelligence: From the Macro to the Nano 305
1 Introduction 305
2 What Is Intelligence? A Macro Perspective 307
2.1 Generalization 307
2.2 Optimization by Nature 313
2.3 Learning from Psychology 315
3 What Is Intelligence? A Micro Perspective 316
3.1 Confabulation-Inspired Association Rule Mining (CARM) 317
4 Social Intelligence? Intelligence Is as a Result of Interaction(Cooperation/Competition) of Several Beings 318
4.1 Fault Detection and Isolation by a Hybrid Fuzzy and Neuro Approach 319
4.2 Distributed Urban Traffic Control and Modeling 319
4.3 What about Shared Autonomy? Social Intelligence with the Human in theLoop 320
5 What Is Intelligence? The Intelligence of the Many 321
5.1 Spin Glasses for Optimization and Portfolio Selection 321
5.2 Swarm Control for Atherosclerosis and Cancer 322
6 Conclusion 325
References 326
Erratum: Control and Systems Engineering 328
Personal Notes 329
Appendix: Mo Jamshidi Publication List 339
Name Index 1