Optimal fuzzy pid controller design of an active magnetic bearing system based on adaptive genetic algorithms

Abstract

This paper proposes an adaptive genetic algorithm (AGA) for the multi-objective optimization design of a fuzzy PID controller and applies it to the control of an active magnetic bearing (AMB) system. Different from PID controllers with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules whose rule consequences employ analytical PID expressions. The PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than the conventional ones. Moreover, it can be easily utilized to develop a precise and fast control algorithm in optimal design. An adaptive genetic algorithm is proposed to design the fuzzy PID controller. The centers of the triangular membership functions and the PID gains for all fuzzy control rules are selected as parameters to be determined. The dynamic model of AMB system for axial motion is also presented. The simulation results of this AMB system show that a fuzzy PID controller designed via the proposed AGA has good performance.