Adaptive fuzzy control with PI learning algorithm for induction servomotor systems

This paper proposes an adaptive fuzzy controller (AFC) with a proportional-integral (PI) learning algorithm for an induction servomotor. The proposed AFC is comprised of a fuzzy controller and a robust controller. The fuzzy controller is to mimic an ideal controller and the robust controller is to dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All the control parameters of the AFC are on-line tuned by a PI learning algorithm in the Lyapunov sense, thus the stability of the system can be guaranteed. Finally, a comparison between a fuzzy controller, an AFC with integral learning algorithm, and the proposed AFC with PI learning algorithm is presented. Simulation results verify that for the induction servomotor systems, the tracking performance of the AFC with PI learning algorithm is better than those of the fuzzy controller and the AFC with integral learning algorithm. Also, the convergence of the tracking error is speeded up.