Adaptive TSKCMAC-identification-based intelligent backstepping control for nonlinear chaotic systems

An adaptive Takagi-Sugeno-Kang type cerebellar model articulation controller (TSKCMAC)-identification-based intelligent backstepping control (ATCIBC) system is proposed for the nonlinear chaotic systems. This ATCIBC system is composed of an adaptive intelligent backstepping controller (AIBC) and a robust H^∞ controller. The AIBC, which uses a TSKCMAC identifier to on-line estimate the controlled system dynamics, is the principal tracking controller. The robust H^∞ controller is designed to attenuate the effect of minimum approximation error introduced by the TSKCMAC identifier and external disturbances with desired attenuation level. Moreover, the all adaptation laws of the ATCIBC system are derived based on the Lyapunov stability analysis, backstepping control technique and H^∞ control theory, so that the stability of the closed-loop system and H^∞ tracking performance can be guaranteed. Finally, the proposed control system is applied to control a Genesio chaotic system. From the simulation results, it is verified that the proposed control scheme can achieve favorable tracking performance for these nonlinear systems.