Constrained predictive control for consensus of nonlinear multi-agent systems by using game Q-learning-Reference-Cited by-同舟云学术

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Constrained predictive control for consensus of nonlinear multi-agent systems by using game Q-learning

Published:2024-12-03 Issue:10 Volume:113 Page:11683-11700
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Wang Yan,Xue Huiwen,Wen Jiwei,Liu Jinfeng,Luan Xiaoli

Funder

China Scholarship Council

Postgraduate Research & Practice Innovation Programof Jiangsu Province

National Natural Science Foundation of China

the China Education Ministry Project 111

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11071-024-10698-5.pdf

Reference33 articles.

1. Jiang, S., Huang, Y., Jafari, M., Jalayer, M.: A distributed multi-agent reinforcement learning with graph decomposition approach for large-scale adaptive traffic signal control. IEEE Trans. Intell. Transp. Syst. 23(9), 14689–14701 (2022)

2. Zhang, Y., Zhou, Y., Lu, H., Fujita, H.: Cooperative multi-agent actor–critic control of traffic network flow based on edge computing. Futur. Gener. Comput. Syst. 123, 128–141 (2021)

3. Joshi, N., Mehta, A.: Distributed discrete-time exponential sliding mode consensus protocol for discrete multi-agent system comprise of multiple robotic arms. ISA Trans. 148, 412–421 (2024)

4. Cui, Yukang, Liang, Yiwen, Luo, Qianyue, Shu, Zhan, Huang, Tingwen: Resilient consensus control of heterogeneous multi-uav systems with leader of unknown input against byzantine attacks. IEEE Trans. Autom. Sci. Eng. (2024). https://doi.org/10.1109/TASE.2024.3420697

5. Guo, Y., Tian, Y., Ji, Y., Ge, Z.: Fixed-time consensus of nonlinear multi-agent system with uncertain disturbances based on event-triggered strategy. ISA Trans. 126, 629–637 (2022)

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