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Quantisation compensated data-driven iterative learning control for nonlinear systems

Author

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  • Huimin Zhang
  • Ronghu Chi
  • Zhongsheng Hou
  • Biao Huang

Abstract

This work presents a quantisation compensation-based data-driven iterative learning control (QC-DDILC) scheme by incorporating a uniform quantiser and an encoding–decoding mechanism (E-DM) to deal with the problem of limited communication resources in a networked control system. Since it is directly aimed at a nonlinear nonaffine system, an iterative dynamic linearisation method is employed to transfer it to a linear data model. Then, the QC-DDILC method is developed by the use of optimisation technique for the learning control law and the parameter updating law, respectively, where the quantised output from the E-DM is used. Since the direct output measurement of the system is unavailable, the linear data model is also acted as an iterative predictive model to estimate the system outputs utilised as the compensator in the consequent QC-DDILC. The proposed QC-DDILC is a data-driven method without relying on any explicit mechanism model information. The convergence analysis is conducted by using the mathematical tools of contraction mapping and induction. Simulations verify the theoretical results.

Suggested Citation

  • Huimin Zhang & Ronghu Chi & Zhongsheng Hou & Biao Huang, 2022. "Quantisation compensated data-driven iterative learning control for nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(2), pages 275-290, January.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:2:p:275-290
    DOI: 10.1080/00207721.2021.1950232
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