Robust Control Optimization with Metaheuristics [Hardcover]

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. In order to do this, the Engineer uses an experimental iterative process (Trial and Error phase) which relies heavily on his or her experience. This book looks to optimise the methods for synthesising servo controllers ny making them more direct and thus quicker to design. This is achieved by calculating a final controller to directly tackle the high-end system specs.

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. lS/