Journal
AUTOMATICA
Volume 49, Issue 8, Pages 2508-2516Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2013.04.039
Keywords
Iterative learning control; Barrier composite energy function; Parametric and nonparametric uncertainty; Alignment condition
Funding
- SERC Research Grant [092 101 00558]
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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 single-input 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. (C) 2013 Elsevier Ltd. All rights reserved.
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