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An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

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  • Shu-Ting Sun
  • Xiao-Dong Li
  • Ren-Xin Zhong

Abstract

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.

Suggested Citation

  • Shu-Ting Sun & Xiao-Dong Li & Ren-Xin Zhong, 2017. "An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(13), pages 2752-2763, October.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:13:p:2752-2763
    DOI: 10.1080/00207721.2017.1346153
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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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