Please use this identifier to cite or link to this item: https://doi.org/10.1109/CDC.2009.5399801
Title: Iterative learning control design with high-order internal model for nonlinear systems
Authors: Yin, C.
Xu, J.-X. 
Hou, Z.
Issue Date: 2009
Source: Yin, C., Xu, J.-X., Hou, Z. (2009). Iterative learning control design with high-order internal model for nonlinear systems. Proceedings of the IEEE Conference on Decision and Control : 434-439. ScholarBank@NUS Repository. https://doi.org/10.1109/CDC.2009.5399801
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 continuoustime nonlinear systems. Utilizing of conventional time-weighted norm method guarantees validity of proposed algorithm in a sense of data-driven control. ©2009 IEEE.
Source Title: Proceedings of the IEEE Conference on Decision and Control
URI: http://scholarbank.nus.edu.sg/handle/10635/70715
ISBN: 9781424438716
ISSN: 01912216
DOI: 10.1109/CDC.2009.5399801
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