Développement d’un contrôleur flou optimal pour des systèmes non linéaires

Abstract

In this memory we have proposed three approaches to design a tracking control for SISO and MIMO nonlinear systems. The first approach is a linear quadratic control where the weighting matrices have been optimized by the particle swarm method. The second approach is an adaptive fuzzy control with its two variants direct and indirect. The third approach is an optimal fuzzy control based on the dynamic Takagi-Sugeno fuzzy models, where the controller was synthesized using the PDC approach by two techniques. The first known as the suboptimal PDC minimizes a linear quadratic performance measure. The second called optimal PDC is based on the minimization of an upper bound on the linear quadratic performance measure using the linear matrix inequality (LMI). The stability of closed-loop systems was studied using Lyapunov synthesis. The obtained simulation results, under the Matlab environment, showed the effectiveness and the performances of the proposed control techniques.