CMAC based integral variable structure control of nonlinear system

Abstract

A CMAC-based controller with a compensating neural network and an update rule is proposed to design the integral variable structure control (IVSC) of a nonlinear system. The control scheme comprises a soft supervisor controller and a CMAC neural network. Based on the Lyapunov theorem, the soft supervisor controller guarantees the global stability of the system. The CMAC neural network provides a compensatory signal to perform the equivalent control by a real-time learning algorithm. The new IVSC control scheme reduced the dependency on system parameters and eliminated the chattering of the control signal through learning. It is proved that the CMAC-based IVSC (CIVSC) scheme is globally stable in the sense that all signals involved are bounded and the tracking error will converge to zero. Simulation results of numerical example demonstrate the effectiveness and robustness of the proposed controller.