Funnel-based optimized formation control for MIMO multi-agent systems under DoS attacks: A DETM quantized method - Xu, Ning; Gao, Zhen; Zhao, Ning; Zhang, Liang (IEEE internet of things journal, 2025)
Combining reinforcement learning (RL) and quantization methods, this article addresses the formation control problem for a class of multi-input multi-output (MIMO) nonlinear multi-agent systems (MASs) subject to denial-of-service (DoS) attacks. Since the information exchange between agents is destroyed by DoS attacks, the leader's information becomes unavailable. Thus, a distributed formation estimator is designed to obtain the leader's state. Next, by constructing an logarithmic quantizer, a new dynamic event-triggered quantized control strategy is studied to save communication resources. In order to satisfy both transient and steady-state performances, an improved funnel function is embedded in controller design. In addition, based on optimized backstepping technique, an actor-critic architecture is established to achieve optimized secure formation control by RL method. Finally, the simulation results verify the effectiveness of proposed control scheme.