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AFD-based ILC designs in frequency domain for linear discrete-time systems

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  • Wen-Yuan Fu
  • Xiao-Dong Li
  • Tao Qian

Abstract

The existing frequency-domain-based iterative learning control (ILC) methods are highly dependent on the mathematical models of the controlled systems. For linear discrete-time single-input single-output (SISO) systems with unknown mathematical models, this paper tries to present fully data-driven ILC designs in frequency domain. With the help of support vector machine (SVM), the input-output data of the linear discrete-time SISO system at the first repetition is utilised to constitute an adaptive Fourier decomposition (AFD) model. Then, based on the AFD model, a P-type ILC law and an extended D-type ILC law with data-driven determining techniques for learning gains are presented. It is noted that comparing with the conventional D-type ILC law, the newly proposed extended D-type ILC law exhibits superior tracking characteristic due to involving the frequency information during the ILC process. A numerical example is utilised to illustrate the effectiveness of the proposed ILC algorithms with the data-driven determining techniques for learning gains.

Suggested Citation

  • Wen-Yuan Fu & Xiao-Dong Li & Tao Qian, 2020. "AFD-based ILC designs in frequency domain for linear discrete-time systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(16), pages 3393-3407, December.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:16:p:3393-3407
    DOI: 10.1080/00207721.2020.1815097
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