Title: Adaptive iterative learning control of nonlinearly parameterised strict feedback systems with input saturation
Authors: Hocine Benslimane; Abdesselem Boulkroune; Hachemi Chekireb
Addresses: LCP, Department of Automatic Control, ENP, BP. 182, 10 Avenue Hassan Badi, El-Harrach, Algiers, Algeria; LAJ, Department of Automatic Control, University of Jijel, BP. 98 Ouled-Aissa, Jijel, Algeria ' LAJ, Department of Automatic Control, University of Jijel, BP. 98 Ouled-Aissa, Jijel, Algeria ' LCP, Department of Automatic Control, ENP, BP. 182, 10 Avenue Hassan Badi, El-Harrach, Algiers, Algeria
Abstract: In this paper, a new adaptive iterative learning control scheme is proposed to deal with nonlinearly parameterised strict feedback systems under alignment condition in the presence of input saturation constraint. The learning controller is designed by using the command filtered adaptive backstepping design procedure. The nonlinearly connected parameters are separated from the local Lipschitz continuous nonlinear functions and then learning laws are designed in iteration domain. To overcome the problem of input saturation, an auxiliary system is constructed with the same order as that of the systems under consideration. It is proved that the proposed control scheme can guarantee that all signals of the resulting closed-loop system remain bounded, and the tracking error converges to zero as the iteration number goes to infinity. A simulation example is included to illustrate the effectiveness of the proposed scheme.
Keywords: adaptive iterative learning control; AILC; Lyapunov functional; nonlinearly parameterised functions; input saturation constraint; backstepping method; strict feedback systems.
International Journal of Automation and Control, 2018 Vol.12 No.2, pp.251 - 270
Received: 05 Jul 2016
Accepted: 14 Oct 2016
Published online: 06 Feb 2018 *