On iterative learning control design for tracking iteration-varying trajectories with high-order internal model

In this paper,iterative learning control (ILC) design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models (HOIM).An HOIM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying.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.Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.