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

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  • Yun-Shan Wei
  • Xiao-Dong Li

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.

Suggested Citation

  • Yun-Shan Wei & Xiao-Dong Li, 2017. "Varying trail lengths-based iterative learning control for linear discrete-time systems with vector relative degree," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(10), pages 2146-2156, July.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:10:p:2146-2156
    DOI: 10.1080/00207721.2017.1309590
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    Cited by:

    1. Yun-Shan Wei & Qing-Yuan Xu, 2018. "Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length," Complexity, Hindawi, vol. 2018, pages 1-6, November.

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