Simulated annealing approach to fuzzy modeling of servo systems

This paper proposes an approach to the fuzzy modeling of servo systems using Simulated Annealing (SA) algorithms. A set of local state-space models is obtained from the first principle models of the process. The initial Takagi-Sugeno-Kang (TSK) fuzzy models are obtained by the modal equivalence principle, where the local state-space models are placed in the rule consequents. Optimization problems are defined aiming the minimization of objective functions expressed as integrals of squared modeling errors. The variables of the objective functions are the limits of the supports of the input membership functions and the kernels of these membership functions are kept constant. SA algorithms are implemented to solve the optimization problems which yield optimal TSK fuzzy models. A set of realtime experimental results for a laboratory nonlinear servo system validates the new optimal TSK fuzzy models.