Please use this identifier to cite or link to this item: https://doi.org/10.1109/TAC.2010.2069372
Title: A high-order internal model based iterative learning control scheme for nonlinear systems with time-iteration-varying parameters
Authors: Yin, C.
Xu, J.-X. 
Hou, Z.
Keywords: High-order internal model
iteration-varying
iterative learning control (ILC)
nonlinear system
parametric uncertainty
Issue Date: Nov-2010
Source: Yin, C., Xu, J.-X., Hou, Z. (2010-11). A high-order internal model based iterative learning control scheme for nonlinear systems with time-iteration-varying parameters. IEEE Transactions on Automatic Control 55 (11) : 2665-2670. ScholarBank@NUS Repository. https://doi.org/10.1109/TAC.2010.2069372
Abstract: In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored. © 2006 IEEE.
Source Title: IEEE Transactions on Automatic Control
URI: http://scholarbank.nus.edu.sg/handle/10635/54248
ISSN: 00189286
DOI: 10.1109/TAC.2010.2069372
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