Sliding mode learning control for nonminimum phase nonlinear systems

A robust sliding mode learning control scheme for a class of nonminimum phase nonlinear systems is newly developed in this paper. It is shown that the proposed controller with a recursive learning mechanism can be designed to drive the sliding variable to reach and remain on the sliding surface. The system states are then guaranteed to asymptotically converge to zero in the sliding mode. Not only is the asymptotic convergence of the input-output dynamics successfully achieved, but also the internal dynamics can be stabilized completely. The developed control scheme exhibits a strong robustness against uncertain dynamics and the controller design does not require the knowledge of the bounds of uncertainties. Simulation results are presented to illustrate the effectiveness of the proposed control methodology.