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 12
Issue 2
IEEE/CAA Journal of Automatica Sinica
| Citation: | Z. Feng and S. Yao, “Dynamic event-triggered active disturbance rejection formation control for constrained underactuated AUVs,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 460–462, Feb. 2025. doi: 10.1109/JAS.2024.124617
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| [1] |
H.L. Wei, C. Shen, and Y. Shi, “Distributed Lyapunov-based model predictive formation tracking control for autonomous underwater vehicles subject to disturbances,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 8, pp. 5198–5208, 2021. doi: 10.1109/TSMC.2019.2946127
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| [2] |
J. Zhang, K. Li, and Y. Li, “Output-feedback based simplified optimized backstepping control for strict-feedback systems with input and state constraints,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1119–1132, 2021. doi: 10.1109/JAS.2021.1004018
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| [3] |
Z. Hao, X. Yue, H. Wen, and C. Liu, “Full-state-constrained non-certainty-equivalent adaptive control for satellite swarm subject to input fault,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 482–495, 2022. doi: 10.1109/JAS.2021.1004216
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| [4] |
K. Zhao and Y. Song, “Removing the feasibility conditions imposed on tracking control designs for state-constrained strict-feedback systems,” IEEE Trans. Autom. Control, vol. 64, no. 3, pp. 1265–1272, 2019. doi: 10.1109/TAC.2018.2845707
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| [5] |
Z. Feng, R. Li, and L. Wu, “Adaptive decentralized control for constrained strong interconnected nonlinear systems and its application to inverted pendulum,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 7, pp. 10110–10120, 2024.
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| [6] |
B. Yang, Q. Zhou, L. Cao, and R. Lu, “Event-triggered control for multi-agent systems with prescribed performance and full state constraints,” Acta Autom. Sinica., vol. 45, no. 8, pp. 1527–1535, 2019.
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| [7] |
X. Ge, Q.-L. Han, X.-M. Zhang, and D. Ding, “Dynamic event-triggered control and estimation: A survey,” Int. J. Autom. Comput., vol. 18, no. 6, pp. 857–886, 2021. doi: 10.1007/s11633-021-1306-z
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G. Zhu, Y. Ma, Z. Li, R. Malekian, and M. Sotelo, “Dynamic event-triggered adaptive neural output feedback control for MSVs using composite learning,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 1, pp. 787–800, 2023. doi: 10.1109/TITS.2022.3217152
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| [9] |
J. Xu, Y. Cui, W. Xing, F. Huang, X. Du, Z. Yan, and D. Wu, “Distributed active disturbance rejection formation containment control for multiple autonomous underwater vehicles with prescribed performance,” Ocean Eng., vol. 259, p. 112057, 2022. doi: 10.1016/j.oceaneng.2022.112057
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| [10] |
J. Du, J. Li, and F. Lewis, “Distributed 3D time-varying formation control of underactuated AUVs with communication delays based on data-driven state predictor,” IEEE Trans. Ind. Inf., vol. 19, no. 5, pp. 6963–6971, 2023. doi: 10.1109/TII.2022.3194632
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| [11] |
K. Zhao and Y. Song, “Neuroadaptive robotic control under time-varying asymmetric motion constraints: A feasibility-condition-free approach,” IEEE Trans. Cybern., vol. 50, no. 1, pp. 15–24, 2020. doi: 10.1109/TCYB.2018.2856747
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P. Ioannou and J. Sun, “Robust adaptive control,” Upper Saddle River, USA: PTR Prentice-Hall, 1996.
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