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 inwhich reference trajectories are generated by high-order internal models (HOIM). An HOIM formulated as a polynomialoperator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes theiterative 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-orderinternal model. By inserting the HOIM into P-type ILC, the tracking performance along the iteration axis is investigatedfor a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposedalgorithm in a sense of data-driven control.