Repetitive Learning Control for a Class of Nonlinear Systems

This paper presents a design method of repetitive learning control for a class of nonlinear uncertain systems. The control design is carried out by the estimation of the desired control and the norm-bounding uncertainty. By the adaptive learning techniques, the desired control is taken as a parametric uncertainty with regressor one. In addition, the variation of the nonlinearity, characterized by the bounding function, can be handled to alleviate the requirement for the knowledge about the system dynamics. The upper bound of the control gain is only required in this scheme. The boundedness of variables in the closed-loop system and the asymptotical convergence of the tracking error are established. And numerical results are presented to demonstrate the effectiveness of the proposed control scheme.