Simulated annealing-based optimization of fuzzy models for magnetic levitation systems

This paper proposes an approach to the Simulated Annealing (SA)-based optimization of fuzzy models for magnetic levitation systems. The unstable and nonlinear processes are first linearized at nine operating points, and next stabilized by a state feedback control system (SFCS) structure. The Takagi-Sugeno (T-S) fuzzy models are obtained by the modal equivalence principle in terms of placing the local SFCS state-space models in the rule consequents. The optimization problems are defined aiming the minimization of objective functions expressed as the squared modeling errors, and the variables of these functions are a part of the parameters of the input membership functions. SA algorithms are involved in solving the optimization problems which give optimal T-S fuzzy models. A set of real-time experimental results validates the fuzzy modeling approach and the new optimal T-S fuzzy models for magnetic levitation system with two electromagnets laboratory equipment.