Adaptive intelligent tracking control using RCMAC for uncertain nonlinear chaotic systems

In this paper, an adaptive intelligent tracking control (AITC) scheme using an adaptive recurrent cerebellar model articulation controller (RCMAC) is developed for a class of uncertain nonlinear chaotic systems. This AITC system offers a design approach to drive the state trajectory to track a desired trajectory, and it is comprised of an adaptive RCMAC and a robust controller. The adaptive RCMAC is used to mimic an ideal sliding mode control, and a robust controller is designed to recover the residual approximation error for guaranteeing the stable characteristic. The all adaptation laws of the AITC system are derived based on the Lyapunov stability analysis, so that the stability of the system can be guaranteed. Finally, the proposed AITC system is applied to control a Van der Pol oscillator and a Genesio chaotic system. The effectiveness of the proposed control scheme is verified by some simulation results with unknown system dynamics and existence of external disturbance.