Abstract:
The work presented in this thesis revolves mainly around the main areas of modeling and fuzzy control of Takagi-Sugeno zero order for dynamic, complex and highly nonlinear systems.
First, a control law has been proposed using several hybrid algorithms based on the
combination of global search algorithms, Genetic Algorithms (GA) and Particle Swarm
Optimization (PSO) and the local search algorithm the Tabu Search (TS). The hybridization consists to combine the characteristics of these methods (GA -TS and PSO-TS) in order to reap the benefits of success for a good solution in a reduced calculation time, all ensuring the stability, accuracy and robustness controlled systems. For each control structure, examples of simulation applications were presented to justify the validity of the proposed approaches.
These results were compared with other techniques cited in references.
In the second phase, the identification of fuzzy models of Takagi-Sugeno zero-order
approximation for the nonlinear systems was performed using the two hybrid algorithms (GA -TS) and (PSO-TS). The modeling results were compared with those existed in the literature through a performance criterion.
