CMAC-based compensator for limiting bound required in supervisory control systems

In this paper, a novel cerebellar model articulation controller (CMAC)-Based compensator is proposed to limit bound required in supervisory control systems. There are two structures in the proposed schemes: one is supervisory controller and the other is the CMAC-Based compensator. The supervisory controller can ensure Lyapunov stability of the controlled system in the presence of significant plant uncertainties, if the perfect control is estimated. The CMAC is employed to learn the perfect control, but a model error will exist in the learning process. The object of CMAC-based compensator is to suppress this model error, so that the supervisory of can be rationalized for uncertain nonlinear systems. Finally, simulation results demonstrate that the CMAC-based compensator not only can limit the bound required in supervisory controllers, but also can significantly improve the control performance.