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Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

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  • Yun-Shan Wei
  • Qing-Yuan Xu

Abstract

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:complx:2763210
    DOI: 10.1155/2018/2763210
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Jun Yang & Jing Na & Guanbin Gao & Chao Zhang, 2018. "Adaptive Neural Tracking Control of Robotic Manipulators with Guaranteed NN Weight Convergence," Complexity, Hindawi, vol. 2018, pages 1-11, October.
    4. Qing-Yuan Xu & Xiao-Dong Li, 2018. "Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(9), pages 1878-1894, July.
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