The classic approach in Automatic Control relies on the use ofsimplified models of the systems and reformulations of thespecifications. In this framework, the control law can be computedusing deterministic algorithms. However, this approach fails whenthe system is too complex for its model to be sufficientlysimplified, when the designer has many constraints to take intoaccount, or when the goal is not only to design a control but alsoto optimize it. This book presents a new trend in Automatic Controlwith the use of metaheuristic algorithms. These kinds of algorithmcan optimize any criterion and constraint, and therefore do notneed such simplifications and reformulations.
The first chapter outlines the author's main motivations forthe approach which he proposes, and presents the advantages whichit offers. In Chapter 2, he deals with the problem of systemidentification. The third and fourth chapters are the core of thebook where the design and optimization of control law, using themetaheuristic method (particle swarm optimization), is given. Theproposed approach is presented along with real-life experiments, proving the efficiency of the methodology. Finally, in Chapter 5, the author proposes solving the problem of predictive control ofhybrid systems.
1. Introduction and Motivations.
2. Symbolic Regression.
3. PID Design Using Particle Swarm Optimization.
4. Tuning and Optimization of H-infinity Control Laws.
5. Predictive Control of Hybrid Systems.
About the Authors
Guillaume Sandou is Professor in the Automatic Department ofSup lec, in Gif Sur Yvette, France. He has had 12 books, 8journal papers and 1 patent published, and has written papers for32 international conferences.His main research interests includemodeling, optimization and control of industrial systems;optimization and metaheuristics for Automatic Control; andconstrained control.