Please use this identifier to cite or link to this item: https://doi.org/10.1002/acs.1163
Title: On iterative learning control with high-order internal models
Authors: Liu, C.
Xu, J. 
Wu, J.
Keywords: High-order internal mode
ILC
Varying reference trajectory
Issue Date: Sep-2010
Source: Liu, C.,Xu, J.,Wu, J. (2010-09). On iterative learning control with high-order internal models. International Journal of Adaptive Control and Signal Processing 24 (9) : 731-742. ScholarBank@NUS Repository. https://doi.org/10.1002/acs.1163
Abstract: In this work we focus on iterative learning control (ILC) for iteratively varying reference trajectories, which are described by a high-order internal models (HOIM) that can be formulated as a polynomials between two consecutive iterations. The classical ILC with iteratively invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a first-order internal model with a unity coefficient. By incorporating HOIM into the ILC law, and designing appropriate learning control gains, the learning convergence in the iteration axis can be guaranteed for continuous-time linear time-varying systems. The initial resetting condition, P-type and D-type ILC, and possible extension to nonlinear cases are also explored in this work. Copyright © 2010 John Wiley & Sons, Ltd.
Source Title: International Journal of Adaptive Control and Signal Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/56871
ISSN: 08906327
DOI: 10.1002/acs.1163
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