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Iterative learning control for 2-D linear discrete Fornasini–Marchesini model with input saturation

Author

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  • Kai Wan
  • Deming Xu
  • Yun-shan Wei

Abstract

In this paper, convergent property of a saturation-strategy-based iterative learning control (ILC) law is first investigated for a class of two-dimensional linear discrete first Fornasini–Marchesini model (2-D LDFFM) with input saturation. A three-dimensional dynamical process is transformed into a 2-D dynamical process by row scanning approach or column scanning approach. As a result, it is theoretical proved no matter which method is adopted, perfect tracking on the desired reference surface is accomplished by virtue of the 2-D linear inequality theory. Numerical simulation on a practical thermal process is used to illustrate the effectiveness and feasibility of the designed ILC law. In addition, ILC convergence analysis for 2-D LDFFM with input delay and input saturation is discussed.

Suggested Citation

  • Kai Wan & Deming Xu & Yun-shan Wei, 2020. "Iterative learning control for 2-D linear discrete Fornasini–Marchesini model with input saturation," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(8), pages 1482-1494, June.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:8:p:1482-1494
    DOI: 10.1080/00207721.2020.1766155
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