Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.automatica.2013.04.039
Title: Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties
Authors: Jin, X.
Xu, J.-X. 
Keywords: Alignment condition
Barrier composite energy function
Iterative learning control
Parametric and nonparametric uncertainty
Issue Date: Aug-2013
Source: Jin, X.,Xu, J.-X. (2013-08). Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties. Automatica 49 (8) : 2508-2516. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2013.04.039
Abstract: In this work, by proposing a Barrier Composite Energy Function (BCEF) method with a novel Barrier Lyapunov Function (BLF), we present a new iterative learning control (ILC) scheme for a class of singleinput single-output (SISO) high order nonlinear systems to deal with output-constrained problems under alignment condition with both parametric and nonparametric system uncertainties. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties satisfying local Lipschitz condition can be effectively handled. Backstepping design with the newly proposed BLF is incorporated in analysis to ensure output constraint not violated. Through rigorous analysis, we show that under this new ILC scheme, uniform convergence of state tracking error is guaranteed. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed ILC scheme. © 2013 Elsevier Ltd. All rights reserved.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/56426
ISSN: 00051098
DOI: 10.1016/j.automatica.2013.04.039
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