Particle Swarm Optimization of fuzzy models for electromagnetic actuated clutch systems

This paper proposes an approach to the optimal fuzzy modeling of a nonlinear servo system application represented by an electromagnetic actuated clutch system. The nonlinear model of the process is first linearized around several operating points of the input-output static map of the process. Discrete-time Takagi-Sugeno (T-S) fuzzy models of the process are derived on the basis of the modal equivalence principle, and the rule consequents of these T-S fuzzy models contain the linearized state-space models of the process. Optimization problems are defined which aim the minimization of objective functions (OFs) defined as the mean of squared modeling errors (the difference between the process output and the fuzzy model output). The variables of the OFs are represented by a part of the parameters of the input membership functions. A Particle Swarm Optimization (PSO) algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of simulation results is included to validate the PSO algorithm-based modeling approach and the optimal T-S fuzzy models for the electromagnetic actuated clutch system.