Iterative Learning Control for Strict-Feedback Nonlinear Systems with Both Structured and Unstructured Uncertainties

摘要

In this paper, the problem of designing a new iterative learning control has been investigated for a class of strict-feedback nonlinear systems subject to both structured and unstructured uncertainties and dynamic disturbances. The considered systems are assumed to perform the same operation repeatedly under alignment condition. Simple learning mechanisms are proposed to estimate the unknown state-dependent nonlinear functions satisfying local Lipschitz conditions. By using the concept of command filtered backstepping, the problem of the explosion of complexity existing in conventional backstepping is eliminated and the proposed controller is greatly simplified. 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 iterations. Simulation results are provided to showthe effectiveness of the proposed control scheme.