High-Order Internal Model Based Barrier Iterative Learning Control for Time-Iteration-Varying Parametric Uncertain Systems With Arbitrary Initial Errors

Abstract

In this paper, a high-order internal model based adaptive iterative learning control scheme is proposed to solve the trajectory tracking problem for a class of nonlinear systems with time-iteration-varying parametric uncertainties which are generated from a high-order internal model. A time-varying boundary layer is constructed to remove the nonzero initial error condition in ILC design. An adaptive iterative learning law is designed to deal with the time-iteration-varying parametric uncertainties. For improving the robustness and safety, a barrier Lyapunov function is adopted to controller design, thus making the filtering error constrained during each iteration. Even there exist nonzero initial state errors, the norm of tracking error vector will asymptotically converge to a tunable residual set as the iteration number increases. Simulation results show the effectiveness of the propose high-order internal model based filtering-error constraint adaptive learning scheme.

Keywords

• High-order internal model
• iterative learning control
• iteration-varying parametric uncertainties
• barrier Lyapunov function
• initial position problem