Iterative learning control design with high-order internal model for nonlinear systems

Abstract

In this work we focus on iterative learning control (ILC) design for tracking iteration-varying reference trajectories that are generated by high-order internal models (HOIM). An HOIM can be formulated as a polynomial operator between consecutive iterations to describe the changes of desired trajectories in the iteration domain. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a unity coefficient or a special first order internal model. By inserting the HOIM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Utilizing of conventional time-weighted norm method guarantees validity of proposed algorithm in a sense of data-driven control.