Varying trail lengths-based iterative learning control for linear discrete-time systems with vector relative degree

Yun-Shan Wei and Xiao-Dong Li

International Journal of Systems Science, 2017, vol. 48, issue 10, 2146-2156

Abstract: In this article, to tackle with the iteration-varying trail lengths and random initial state shifts, an average operator-based PD-type iterative learning control (ILC) law is firstly presented for linear discrete-time multiple-input multiple-output (MIMO) systems with vector relative degree. The proposed PD-type ILC law includes an initial rectifying action against initial state shifts, and pursues the reference trajectory tracking beyond the initial time points. As special cases of the PD-type ILC law, P-type and D-type ILC laws are then introduced. It is proved that for linear discrete-time MIMO systems with vector relative degree, the three proposed ILC laws can drive the varying trail lengths-based ILC tracking errors to zero in mathematical expectation beyond the initial time points. A numerical example is used to illustrate the effectiveness of the proposed ILC laws.

Date: 2017
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DOI: 10.1080/00207721.2017.1309590

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