An Adaptive Fuzzy Cerebellar Model Articulation Controller for Synchronization of Chaotic Systems

This paper aims to propose an adaptive fuzzy cerebellar model articulation controller (FCMAC) for synchronization of two chaotic systems in the presence of uncertainties and external disturbance. The proposed controller is comprised of parametric fuzzy rules. The antecedent part of every rule remain steady, while the subsequent part is renewed with every time step by establishing adaptation laws. These adaptation laws are derived by the Lyapunov synthesis approach and guarantee the closed-loop system's stability. Simplicity, low memory demand and rapid control satisfy the practical usage of the adaptive FCMAC. Furthermore, adaptation laws can overcome lack of human experts knowledge about the chaotic plant. Eventually, simulations for synchronization of two Duffing-Holmes chaotic systems reveal that a satisfactory tracking performance can be achieved by using the proposed control system.