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IEEE/CAA Journal of Automatica Sinica

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Z.-H. Pang, Y. Zhang, X. Sun, S. Gao, and G.-P. Liu, “Data-driven adaptive predictive control method with autotuned weighting factor for nonlinear systems using triangular dynamic linearization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1–3, Aug. 2024.
Citation: Z.-H. Pang, Y. Zhang, X. Sun, S. Gao, and G.-P. Liu, “Data-driven adaptive predictive control method with autotuned weighting factor for nonlinear systems using triangular dynamic linearization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1–3, Aug. 2024.

Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

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  • [1]
    H. Wei and Y. Shi, “MPC-based motion planning and control enables smarter and safer autonomous marine vehicles: Perspectives and a tutorial survey,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 8–24, Jan. 2023. doi: 10.1109/JAS.2022.106016
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    L. Zhou, M. Eull, and M. Preindl, “Optimization-based estimation and model predictive control for high performance, low cost software-defined power electronics,” IEEE Trans. Power Electron., vol. 38, no. 1, pp. 1022–1035, Jan. 2023. doi: 10.1109/TPEL.2022.3202863
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    Z.-H. Pang, W.-C. Luo, G.-P. Liu, and Q.-L. Han, “Observer-based incremental predictive control of networked multi-agent systems with random delays and packet dropouts,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 68, no. 1, pp. 426–430, Jan. 2021.
    [4]
    Z.-H. Pang, X.-Y. Zhao, J. Sun, Y.-T. Shi, and G.-P. Liu, “Comparison of three data-driven networked predictive control methods for a class of nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1714–1716, Sept. 2022. doi: 10.1109/JAS.2022.105830
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    J. Berberich, J. Kóhler, M. A. Múller, and F. Allgower, “Data-driven model predictive control with stability and robustness guarantees,” IEEE Trans. Autom. Control, vol. 66, no. 4, pp. 1702–1717, Apr. 2021. doi: 10.1109/TAC.2020.3000182
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    A. D. Carnerero, D. R. Ramirez, D. Limon, and T. Alamo, “Kernel-based state-space kriging for predictive control,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1263–1275, May 2023. doi: 10.1109/JAS.2023.123459
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    M. P. Polverini, S. Formentin, L. Merzagora, and P. Rocco, “Mixed data-driven and model-based robot implicit force control: A hierarchical approach,” IEEE Trans. Control Syst. Technol., vol. 28, no. 4, pp. 1258–1271, Jul. 2020. doi: 10.1109/TCST.2019.2908899
    [8]
    Z. Li, X. Yuan, Y. Wang, and C.-H. Xie, “Subspace predictive control with the data-driven event-triggered law for linear time-invariant systems,” J. Franklin Inst-. Eng. Appl. Math., vol. 356, no. 15, pp. 8167–8181, Oct. 2019. doi: 10.1016/j.jfranklin.2019.07.009
    [9]
    Z. Hou, S. Liu, and T. Tian, “Lazy-learning-based data-driven model-free adaptive predictive control for a class of discrete-time nonlinear systems,” IEEE Trans. Neural Networks Learn. Syst., vol. 28, no. 8, pp. 1914–1928, Aug. 2017. doi: 10.1109/TNNLS.2016.2561702
    [10]
    Z.-H. Pang, B. Ma, G.-P. Liu, and Q.-L. Han, “Data-driven adaptive control: An incremental triangular dynamic linearization approach,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 69, no. 12, pp. 4949–4953, Dec. 2022.

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