Ben Attia Selma*, Ouerfelli Houssem Eddine, Salhi Salah
Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 795-810, 2022, DOI:10.32604/iasc.2022.020435
Abstract This study addressed sufficient conditions for the robust monotonic convergence of repetitive discrete-time linear parameter varying systems, with the parameter variation rate bound. The learning law under consideration is an anticipatory iterative learning control. Of particular interest in this study is that the iterations can eliminate the influence of disturbances. Based on a simple quadratic performance function, a sufficient condition for the proposed learning algorithm is presented in terms of linear matrix inequality (LMI) by imposing a polytopic structure on the Lyapunov matrix. The set of LMIs to be determined considers the bounds on the rate of variation of the… More >
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