The classic approach in Automatic Control relies on the use of
simplified models of the systems and reformulations of the
specifications. In this framework, the control law can be computed
using deterministic algorithms. However, this approach fails when
the system is too complex for its model to be sufficiently
simplified, when the designer has many constraints to take into
account, or when the goal is not only to design a control but also
to optimize it. This book presents a new trend in Automatic Control
with the use of metaheuristic algorithms. These kinds of algorithm
can optimize any criterion and constraint, and therefore do not
need such simplifications and reformulations.
The first chapter outlines the author?s main motivations for
the approach which he proposes, and presents the advantages which
it offers. In Chapter 2, he deals with the problem of system
identification. The third and fourth chapters are the core of the
book where the design and optimization of control law, using the
metaheuristic method (particle swarm optimization), is given. The
proposed 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 of
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 of
Supélec, in Gif Sur Yvette, France. He has had 12 books, 8
journal papers and 1 patent published, and has written papers for
32 international conferences.His main research interests include
modeling, optimization and control of industrial systems;
optimization and metaheuristics for Automatic Control; and