Particle Swarm Optimization: An efficient tool for the design of Automatic Control laws

The design of automatic control laws is often based on the solution of optimization problems. Costs and constraints of these problems are mainly non convex, non smooth or non analytic. The classical approach is to simplify the problem so as to get a tractable and exactly solvable optimization problem. In this paper, a Particle Swarm Optimization method is used to solve complex initial problems. Examples of PID tuning, reduced order robust H_∞ synthesis and non linear model predictive control are given. Promising results are obtained and show that metaheuristic optimization algorithms could be of great interest for the design of Automatic Control laws.