Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations. Issue 5 (3rd April 2016)

• Record Type: Journal Article
• Authors: Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun
• Is Part Of: International journal of systems science. Volume 47:Issue 5(2016)
• Page Start: 1084
• Page End: 1094
• Publication Date: 2016-04-03
• Subjects: adaptive iterative learning control (AILC) -- non-linearly parameterised systems -- composite energy function (CEF) -- input saturations; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2014.911422
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : 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 2 [0, T ] 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.