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Resilient Cooperative Optimal Output Regulation Control for Nonlinear Multiagent Systems

Ying Xu et al.

IEEE Transactions on Cybernetics2026https://doi.org/10.1109/tcyb.2025.3645097article
AJG 3
Weight
0.37

What the paper says

This article addresses the resilient cooperative optimal output regulation (COOR) control problem for nonlinear strict-feedback multiagent systems (MASs) under denial-of-service (DoS) attacks. By constructing the resilient adaptive distributed observers, the leader's dynamics and states can be estimated by each follower. In the control design, a control input constructed by feedforward and feedback control input is proposed based on the system data. Neural networks (NNs) are employed to learn solutions of the feedforward and optimal feedback control problems. Meanwhile, to handle the influence caused by unknown nonlinear dynamics, combining off-policy integral reinforcement learning (IRL) algorithm with actor-critic NNs (A-C NNs), an optimal feedback security control law is designed. To illustrate the feasibility and effectiveness of the proposed optimal control strategy, numerical and practical simulation examples are provided. Unlike prior studies limited to linear systems, this work explicitly accounts for complex nonlinear dynamics, significantly broadening the applicability of resilient COOR control problem in real-world applications.

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https://doi.org/https://doi.org/10.1109/tcyb.2025.3645097

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@article{ying2026,
  title        = {{Resilient Cooperative Optimal Output Regulation Control for Nonlinear Multiagent Systems}},
  author       = {Ying Xu et al.},
  journal      = {IEEE Transactions on Cybernetics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tcyb.2025.3645097},
}

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Resilient Cooperative Optimal Output Regulation Control for Nonlinear Multiagent Systems

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.