Iterative Learning Control for Nonlinear Systems Subject to both Structured and Unstructured Uncertainties

This paper deals with the problem of designing a new iterative learning control (ILC) for a class of strict-feedback nonlinear systems subject to both structured and unstructured uncertainties and dynamic disturbances. These systems are assumed to perform the same task repeatedly under alignment condition. Simple learning mechanisms are proposed to approximate the unknown nonlinear state-dependent functions satisfying local Lipschitz conditions. Novel dynamical robust control terms are designed to guarantee the stability of the closed-loop system. By using the concept of command filtred backstepping, the problem of the explosion of complexity is eliminated and the proposed backstepping controller is greatly simplified and the amount of computations required are efficiently reduced. Lyapunov-Like functional method is used to prove the boundedness of all signals of the resulting closed-loop system and the convergence of the tracking errors to zero over iteration. A numerical simulation is performed to verify the effectiveness of the proposed control algorithm.