A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 13 Issue 8
Aug.  2026

IEEE/CAA Journal of Automatica Sinica

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J.-G. Zhao, D. Wang, J. Zhao, and G. Chen, “Data-driven reinforcement learning and approximate optimal control for linear time-varying systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1997–1999, Aug. 2026. doi: 10.1109/JAS.2025.125639
Citation: J.-G. Zhao, D. Wang, J. Zhao, and G. Chen, “Data-driven reinforcement learning and approximate optimal control for linear time-varying systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1997–1999, Aug. 2026. doi: 10.1109/JAS.2025.125639

Data-Driven Reinforcement Learning and Approximate Optimal Control for Linear Time-Varying Systems

doi: 10.1109/JAS.2025.125639
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    D. Liu, S. Xue, B. Zhao, B. Luo, and Q. Wei, “Adaptive dynamic programming for control: A survey and recent advances,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 1, pp. 142–160, 2021. doi: 10.1109/TSMC.2020.3042876
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    B. Pang, Z. P. Jiang, and I. Mareels, “Reinforcement learning for adaptive optimal control of continuous-time linear periodic systems,” Automatica, vol. 118, Art. no. 109035, 2020. doi: 10.1016/j.automatica.2020.109035
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