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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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Y. Hu, C. Zhang, B. Wang, J. Zhao, X. Gong, J. Gao, and  H. Chen,  “Noise-tolerant ZNN-Based data-driven iterative learning control for discrete nonaffine nonlinear MIMO repetitive systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 344–361, Feb. 2024. doi: 10.1109/JAS.2023.123603
Citation: Y. Hu, C. Zhang, B. Wang, J. Zhao, X. Gong, J. Gao, and  H. Chen,  “Noise-tolerant ZNN-Based data-driven iterative learning control for discrete nonaffine nonlinear MIMO repetitive systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 344–361, Feb. 2024. doi: 10.1109/JAS.2023.123603

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

doi: 10.1109/JAS.2023.123603
Funds:  This work was supported by the National Natural Science Foundation of China (U21A20166), in part by the Science and Technology Development Foundation of Jilin Province (20230508095RC), in part by the Development and Reform Commission Foundation of Jilin Province (2023C034-3), and in part by the Exploration Foundation of State Key Laboratory of Automotive Simulation and Control
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  • Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output (MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control (ILC) scheme based on the zeroing neural networks (ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer (IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise, an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

     

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    Highlights

    • Novel data-driven iterative learning decoupling control for complex MIMO systems
    • ILC based on NT-ZNNs: global convergence, noise sensitivity reduction
    • The elimination of the assumption about initialization conditions

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