A journal of IEEE and CAA , publishes
high-quality papers in English on original
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Volume 12
Issue 9
IEEE/CAA Journal of Automatica Sinica
| Citation: | R. Ke, J. Tang, Z. Zuo, and Y. Shi, “Koopman-based robust model predictive control with online identification for nonlinear dynamical systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1947–1949, Sept. 2025. doi: 10.1109/JAS.2025.125546
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S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Discovering governing equations from data by sparse identification of nonlinear dynamical systems,” Proc. Nat. Academy Sci., vol. 113, no. 15, pp. 3932–3937, Apr. 2016. doi: 10.1073/pnas.1517384113
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J. Jia, W. Zhang, K. Guo, J. Wang, X. Yu, Y. Shi, and L. Guo, “Evolver: Online learning and prediction of disturbances for robot control,” IEEE Trans. Robot., vol. 40, pp. 382–402, 2024. doi: 10.1109/TRO.2023.3326318
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H. Lee, R. Ren, Y. Qian, and J. Rosen, “Energy reduction for wearable pneumatic valve system with sindy and time-variant model predictive control,” IEEE/ASME Trans. Mechatron., vol. 30, no. 2, pp. 862–872, 2025. doi: 10.1109/TMECH.2024.3458092
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J. Tang and Z. Zuo, “Control-barrier-function-based cooperative guidance with nonuniform field of view and input constraints,” J. Guidance, Cont., and Dyn., vol. 47, no. 11, pp. 2444–2452, 2024. doi: 10.2514/1.G008189
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Z. Zuo, J. Tang, R. Ke, and Q.-L. Han, “Hyperbolic tangent function-based protocols for global/semi-global finite-time consensus of multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1381–1397, 2024. doi: 10.1109/JAS.2024.124485
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X. Zhang, W. Pan, R. Scattolini, S. Yu, and X. Xu, “Robust tube-based model predictive control with Koopman operators,” Automatica, vol. 137, p. 110114, Mar. 2022. doi: 10.1016/j.automatica.2021.110114
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M. Hertneck, J. Kohler, S. Trimpe, and F. Allgower, “Learning an approximate model predictive controller with guarantees,” IEEE Contr. Syst. Lett., vol. 2, no. 3, pp. 543–548, Jul. 2018. doi: 10.1109/LCSYS.2018.2843682
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Y. Li, K. Wu, and J. Liu, “Discover governing differential equations from evolving systems,” Physical Review Research, vol. 5, no. 2, p. 23126, May 2023. doi: 10.1103/PhysRevResearch.5.023126
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X. Gong, X. Wang, and G. Joos, “An online data-driven method for microgrid secondary voltage and frequency control with ensemble koopman modeling,” IEEE Trans. Smart Grid, vol. 14, no. 1, pp. 68–81, Jan. 2023. doi: 10.1109/TSG.2022.3190237
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M. Korda and I. Mezić, “Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,” Automatica, vol. 93, pp. 149–160, Jul. 2018. doi: 10.1016/j.automatica.2018.03.046
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H. Arbabi, M. Korda, and I. Mezic, “A data-driven koopman model predictive control framework for nonlinear partial differential equations,” in Proc. IEEE Conf. Deci. and Contr., Miami Beach, USA, 2018, pp. 6409–6414.
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S. L. Brunton, M. Budišić, E. Kaiser, and J. N. Kutz, “Modern Koopman theory for dynamical systems,” SIAM Rev., vol. 64, no. 2, pp. 229–340, May 2022. doi: 10.1137/21M1401243
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H. Shi and M. Q.-H. Meng, “Deep koopman operator with control for nonlinear systems,” IEEE Robotics and Autom. Lett., vol. 7, no. 3, pp. 7700–7707, Jul. 2022. doi: 10.1109/LRA.2022.3184036
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U. V. Luxburg and B. Schölkopf, “Statistical learning theory: Models, concepts, and results,” in Handbook of the History of Logic. North-Holland, USA: Elsevier, 2011, vol. 10, pp. 651–706.
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J. B. Rawlings, D. Q. Mayne, and M. Diehl, Model Predictive Control: Theory, Computation and Design, 2nd ed. Santa Barbara, USA: Nob Hill Publishing, LLC, 2020.
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D. Mayne, M. Seron, and S. Raković, “Robust model predictive control of constrained linear systems with bounded disturbances,” Automatica, vol. 41, no. 2, pp. 219–224, Feb. 2005. doi: 10.1016/j.automatica.2004.08.019
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