Adaptive fuzzy control using PID-type learning algorithm

Abstract

This paper proposes a proportional-integral-derivative (PID)-learning-type adaptive fuzzy controller (AFC) for chaotic Duffing dynamic systems. The proposed PID-learning-type AFC is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All the control parameters are on-line tuned in the sense of Lyapunov theorem, thus the stability of the system can be guaranteed. Finally, a comparison between a conventional AFC and the proposed PID-learning-type AFC is presented. Simulation results verify that the proposed PID-learning-type AFC can achieve better tracking performance and faster tracking error convergence than the conventional AFC for chaotic Duffing dynamic systems.