Event-Triggered RNN-Based Resilient Model Predictive Consensus Control for Nonlinear Multiagent Systems Under DoS...

This article addresses the problem of resilient consensus control for nonlinear multiagent systems (MASs) operating over networks subject to denial-of-service (DoS) attacks. We propose an event-triggered, recurrent-neural-network-based model predictive consensus controller (RNN-MPCC) that integrates three components: 1) a data-driven recurrent neural network (RNN) predictor of agent dynamics with a certified uniform one-step error bound; 2) a DoS-aware communication model that constrains attack frequency and duration and is embedded in the resilient consensus and update logic; and 3) an aperiodic (event-triggered) execution that reduces transmissions and solver calls while preventing Zeno behavior via a minimum interevent time. The predictive controller optimizes a receding-horizon-based cost function that penalizes the disagreement vector, control effort, and hold-induced errors, enforces input and state constraints, and constructs effective Laplacians from successfully received packets and locally held neighbor copies. Finally, simulations on a six-agent leader-follower multi-UAV network demonstrate that the proposed event-triggered RNN-MPCC achieves resilient consensus under DoS attacks.