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Robust data-driven frequency-domain-based ILC designs for non-repetitive linear discrete-time systems

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

Abstract

This article introduces two robust data-driven iterative learning control (ILC) laws for linear discrete-time systems (LDTSs) with single input and single output (SISO), considering non-repetitive uncertainties in initial conditions, reference trajectories, and external disturbances. The proposed robust ILC laws are developed in frequency domain by using an innovative adaptive Fourier decomposition (AFD) method to approximate the unknown transfer function. They are entirely data-driven in the sense that the input and output (I/O) data of the controlled LDTS are utilized only without requiring any model knowledge beyond the minimum phase feature of the system. Consequently, the ILC tracking errors can be confined within a bounded region whose size can be adjusted by a suitable selection of learning gains. Notably, as the iteration-variant initial conditions, reference trajectories, and system disturbances are progressively repetitive, the designed ILC schemes can ultimately achieve perfect tracking of reference trajectories. Numerical simulations validate the presented robust data-driven ILC laws.

Suggested Citation

  • Fu, Wen-Yuan & Li, Xiao-Dong & Qian, Tao, 2025. "Robust data-driven frequency-domain-based ILC designs for non-repetitive linear discrete-time systems," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).
    • Handle: RePEc:eee:chsofr:v:199:y:2025:i:p1:s0960077925006976
    DOI: 10.1016/j.chaos.2025.116684
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    References listed on IDEAS

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    1. Zhou, Min & Wang, JinRong & Shen, Dong, 2023. "Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Chen, Zhengquan & Hou, Yandong & Huang, Ruirui & Cheng, Qianshuai, 2024. "Neural network compensator-based robust iterative learning control scheme for mobile robots nonlinear systems with disturbances and uncertain parameters," Applied Mathematics and Computation, Elsevier, vol. 469(C).
    3. 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.
    4. Zhou, Xingyu & Tian, Yang & Wang, Haoping, 2022. "Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays," Applied Mathematics and Computation, Elsevier, vol. 430(C).
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