Nash equilibrium seeking of general linear multi-agent systems in the cooperation–competition network

Chen, Zhao; Nian, Xiaohong; Meng, Qinghao

doi:10.1016/j.sysconle.2023.105510

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The successful completion of complex tasks through cooperation in MASs is contingent upon the normal operation of each agent and the maintenance of regular communication connections between agents [7]. However, unlike the single intelligent system described by ordinary differential equations (ODEs) in [8][9][10], with an increase in the number of agents, the long-term operation of MASs becomes more susceptible to the influence of failures in practice [11].…”

Section: Introductionmentioning

confidence: 99%

Distributed Fault Diagnosis via Iterative Learning for Partial Differential Multi-Agent Systems with Actuators

Wang,

Zhou,

Wang

2024

Mathematics

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Component failures can lead to performance degradation or even failure in multi-agent systems, thus necessitating the development of fault diagnosis methods. Addressing the distributed fault diagnosis problem in a class of partial differential multi-agent systems with actuators, a fault estimator is designed under the introduction of virtual faults to the agents. A P-type iterative learning control protocol is formulated based on the residual signals, aiming to adjust the introduced virtual faults. Through rigorous mathematical analysis utilizing contraction mapping and the Bellman–Gronwall lemma, sufficient conditions for the convergence of this protocol are derived. The results indicate that the learning protocol ensures the tracking of virtual faults to actual faults, thereby facilitating fault diagnosis for the systems. Finally, the effectiveness of the learning protocol is validated through numerical simulation.

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