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Received:January 20, 2010Revised:January 20, 2010
基金项目:This work was supported by the General Program (No.60774022), the State Key Program of National Natural Science Foundation of China (No.60834001), and the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University (No.RCS2009ZT011).
On iterative learning control design for tracking iteration-varying trajectories with high-order internal model
Chenkun YIN,Jianxin XU,Zhongsheng HOU
(Beijing Jiaotong University;National University of Singapore)
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.
Key words:  ILC  High-order internal model  Iteration-varying  Nonlinear systems  Continuous-time