Evolving fuzzy models for the position control of magnetic levitation systems

Abstract

This paper proposes evolving Takagi-Sugeno (T-S) fuzzy models that characterize the nonlinear dynamics phenomena occurring in the position of magnetic levitation systems. A state feedback control structure is first designed to stabilize the nonlinear process by linearization at certain operating points, and the evolving T-S fuzzy models are next derived for the stabilized closed-loop system. The rule bases and the parameters of the T-S fuzzy models are evolved by an incremental online identification algorithm (OIA). Real-time experiments are conducted in order to validate the evolving T-S fuzzy models that give the sphere position in magnetic levitation system laboratory equipment. The experimental results prove the very good performance of the T-S fuzzy models in terms of output responses and root mean square error values. The performance comparison with similar T-S fuzzy models evolved by another incremental OIA and three nature-inspired optimization algorithms is included.