Please use this identifier to cite or link to this item: https://doi.org/10.1109/CDC.2012.6426998
Title: Iterative learning control for systems with nonparametric uncertainties under alignment condition
Authors: Jin, X.
Huang, D.
Xu, J.-X. 
Issue Date: 2012
Source: Jin, X.,Huang, D.,Xu, J.-X. (2012). Iterative learning control for systems with nonparametric uncertainties under alignment condition. Proceedings of the IEEE Conference on Decision and Control : 3942-3947. ScholarBank@NUS Repository. https://doi.org/10.1109/CDC.2012.6426998
Abstract: In this work, by incorporating the so-called alignment condition, a novel ILC scheme is proposed for a class of nonlinear systems with nonparametric local Lipschitz continuous (LLC) uncertainties to perform full state tracking tasks. A new Lyapunov-like energy function is adopted to facilitate the ILC design as well as property analysis, and thus achieve the asymptotical convergence of tracking error. More advantages of the proposed design approach lie in that it can handle the scenarios of state-dependent LLC input gain and high-order systems easily. In the end, an illustrative example is simulated to demonstrate the efficacy of the proposed ILC scheme. © 2012 IEEE.
Source Title: Proceedings of the IEEE Conference on Decision and Control
URI: http://scholarbank.nus.edu.sg/handle/10635/83874
ISSN: 01912216
DOI: 10.1109/CDC.2012.6426998
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