Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 47, Issue 5, Pages 1084-1094Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2014.911422
Keywords
adaptive iterative learning control (AILC); non-linearly parameterised systems; composite energy function (CEF); input saturations
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Funding
- International Cooperation Programme of National Science Foundation of China [61120106009]
- Fundamental Research Funds for the Central Universities [2012JBM008, 2013YJS013]
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In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L-[0, T](2) convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.
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