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 9
IEEE/CAA Journal of Automatica Sinica
| Citation: | L. Fan, X. Liu, and W. Yu, “Reinforcement learning-based predefined-time optimal bipartite consensus of high-order multiagent systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2175–2177, Sep. 2026. doi: 10.1109/JAS.2025.125675
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D. Wang, J. Qiao, and L. Cheng, “An approximate neuro-optimal solution of discounted guaranteed cost control design,” IEEE Trans. Cybern., vol. 52, no. 1, pp. 77−86, 2022. doi: 10.1109/TCYB.2020.2977318
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| [2] |
L. Xia, Q. Li, R. Song, and H. Modares, “Optimal synchronization control of heterogeneous asymmetric input-constrained unknown nonlinear MASs via reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 520−532, 2022. doi: 10.1109/JAS.2021.1004359
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| [3] |
X. Li, D. He, Q. Zhang, and H. Liu, “Reinforcement learning-based adaptive event-triggered control of multi-agent systems with time-varying dead-zone,” Appl. Math. Comput., vol. 486, Art. no. 129059, 2025.
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| [4] |
D. Li and J. Dong, “Fuzzy control based on reinforcement learning and subsystem error derivatives for strict-feedback systems with an observer,” IEEE Trans. Fuzzy Syst., vol. 31, no. 8, pp. 2509−2521, 2023. doi: 10.1109/TFUZZ.2022.3227993
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| [5] |
A. Luo, Q. Zhou, H. Ma, and H. Li, “Observer-based consensus control for MASs with prescribed constraints via reinforcement learning algorithm,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 12, pp. 17281−17291, 2024. doi: 10.1109/TNNLS.2023.3301538
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| [6] |
H. Shen, W. Zhao, J. Cao, J. H. Park, and J. Wang, “Predefined-time event-triggered tracking control for nonlinear servo systems: A fuzzy weight-based reinforcement learning scheme,” IEEE Trans. Fuzzy Syst., vol. 32, no. 8, pp. 4557−4569, 2024. doi: 10.1109/TFUZZ.2024.3403917
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| [7] |
Y. Zhao, Y. Zhou, Y. Liu, G. Wen, and P. Huang, “Fixed-time bipartite synchronization with a pre-appointed settling time over directed cooperative-antagonistic networks,” Automatica, vol. 123, Art. no. 109301, 2021. doi: 10.1016/j.automatica.2020.109301
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J. Liang, X. Bu, L. Cui, and Z. Hou, “Event-triggered asymmetric bipartite consensus tracking for nonlinear multi-agent systems based on model-free adaptive control,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 662−672, 2023. doi: 10.1109/JAS.2022.106070
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C. Altafini, “Consensus problems on networks with antagonistic interactions,” IEEE Trans. Autom. Control, vol. 58, no. 4, pp. 935−946, 2013. doi: 10.1109/TAC.2012.2224251
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J. Cheng, Y. Wu, Z. Wu, and H. Yan, “Nonstationary filtering for fuzzy Markov switching affine systems with quantization effects and deception attacks,” IEEE Trans. Syst., Man, Cybern. Syst., vol. 52, no. 10, pp. 6545−6554, 2022. doi: 10.1109/TSMC.2022.3147228
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Z. Zuo and L. Tie, “A new class of finite-time nonlinear consensus protocols for multi-agent systems,” Int. J. Control, vol. 87, no. 2, pp. 363−370, 2014. doi: 10.1080/00207179.2013.834484
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| [12] |
Q. Wang, J. Cao, and H. Liu, “Adaptive fuzzy control of nonlinear systems with predefined time and accuracy,” IEEE Trans. Fuzzy Syst., vol. 30, no. 12, pp. 5152−5165, 2022. doi: 10.1109/TFUZZ.2022.3169852
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