Robust Control Optimization with Metaheuristics

In the automotive industry, a Control Engineer must design a unique control law that is then tested and validated on a single prototype with a level of reliability high enough to to meet a number of complex specifications on various systems.

List of contents

Preface  ix
Introduction and Motivations  xi
Chapter 1. Metaheuristics for Controller Optimization  1
1.1. Introduction  1
1.2. Evolutionary approaches using differential evolution  2
1.2.1. Standard version 2
1.2.2. Perturbed version 7
1.3. Swarm approaches 8
1.3.1. Particle swarm optimization algorithm 8
1.3.2. Quantum particle swarm algorithm 14
1.3.3. Artificial bee colony optimization algorithm  20
1.3.4. Cuckoo search algorithm 25
1.3.5. Firefly algorithm 31
1.4. Summary  33
Chapter 2. Reformulation of Robust Control Problems for Stochastic Optimization  35
2.1. Introduction  35
2.2. H¿ synthesis  35
2.2.1. Full H¿ synthesis 35
2.2.2. Fixed-structure H¿ synthesis 45
2.2.3. Formulating H¿ synthesis for stochastic optimization 67
2.2.4. Conclusion 105
2.3. ¿-Synthesis 105
2.3.1. The problem of performance robustness  105
2.3.2. ¿-Synthesis  110
2.4. LPV/LFT synthesis  140
2.4.1. Introduction  140
2.4.2. The LPV/LFT controller synthesis problem  141
2.4.3. Reformulation for stochastic optimization 147
Chapter 3. Optimal Tuning of Structured and Robust H¿ Controllers Against High-level Requirements 171
3.1. Introduction and motivations 171
3.2. Loop-shaping H¿ synthesis  180
3.2.1. Approach principle  180
3.2.2. Generalized gain and phase margins  184
3.2.3. Four-block interpretation of the method  185
3.2.4. Practical implementation 186
3.2.5. Implementation of controllers  190
3.3. A generic method for the declination of requirements 194
3.3.1. General principles 194
3.3.2. Special cases 196
3.3.3. Management of requirement priority level 197
3.4. Optimal tuning of weighting functions 198
3.4.1. Optimization on nominal plant 198
3.4.2. Multiple plant optimization 202
3.4.3. Applicative example - inertial stabilization of line of sight  207
3.5. Optimal tuning of the fixed-structure and fixed-order final controller 238
3.5.1. Introduction  238
3.5.2. Toward eliminating weighting functions  240
3.5.3. Extensions to the approach  259
3.5.4. Link with standard control problems  277
Chapter 4. HinfStoch: A Toolbox for Structured and Robust Controller Computation Based on Stochastic Optimization 279
4.1. Introduction  279
4.2. Structured multiple plant H¿ synthesis 280
4.2.1. Principle  280
4.2.2. Formalism 280
4.3. Structured ¿-synthesis 284
4.3.1. Principle  284
4.3.2. Formalism 285
4.4. Structured LPV/LFT synthesis  288
4.4.1. Principle  288
4.4.2. Formalism 289
4.5. Structured and robust synthesis against high-level requirements with HinfStoch_ControllerTuning 292
4.5.1. Principle  292
4.5.2. Formalism 293
4.5.3. Examples 311
Appendices 351
Appendix A. Notions of Coprime Factorizations  353
Appendix B. Examples of LFT Form Used for Uncertain Systems  359
Appendix C. LFT Form Use of an Electromechanical System with Uncertain Flexible Modes  365
Appendix D. FTM (1D) Computation from a Time Signal 383
Appendix E. Choice of Iteration Number for CompLeib Tests  385
Appendix F. PDE versus DE 393
Bibliography 399
Index 407

About the author

Philippe Feyel is an R&D Engineer for the high-tech company Sagem Défense Sécurité, part of the defence and security business of the SAFRAN group, in Paris, France.

Summary

In the automotive industry, a Control Engineer must design a unique control law that is then tested and validated on a single prototype with a level of reliability high enough to to meet a number of complex specifications on various systems.

Product details

• Authors: P Feyel, Philippe Feyel
• Publisher: Wiley
• Languages: English
• Product format: Hardback
• Released: 28.02.2017
• EAN: 9781786300423
• ISBN: 978-1-78630-042-3
• No. of pages: 448
• Dimensions: 161 mm x 240 mm x 29 mm
• Weight: 839 g
• Subject: Natural sciences, medicine, IT, technology > Technology > General, dictionaries