A Performance Recovery Approach for Multiagent Systems With Actuator Faults in Noncooperative Games
Hao Wang, Hao Luo, Yuchen Jiang, Okyay Kaynak · IEEE Transactions on Industrial Informatics · 2024
This article proposes a distributed performance recovery method for multiagent systems with actuator faults in noncooperative games. The local agent (player) can only obtain the policy information of neighboring agents through the communication network. The strategies of nonneighbors in the cost function are unknown, and a leader–follower consensus algorithm is introduced to estimate nonneighbors' strategy. When the actuator faults occur in any agents and lead to performance degradation, i.e., the agents' strategy is biased from its optimal strategy. A distributed optimization control method is proposed to recover performance without changing the original control scheme. An observer-based residual feedback plug-and-play optimization method is used to ensure that the strategies of all agents can still converge to the optimal strategy (or close to the optimal strategy). Numerical case studies are applied to demonstrate the performance and effectiveness of the proposed method.