Stackelberg-Game-Based Dynamic Event-Triggered Distributed Model Predictive Control Approach for Multiagent Systems Under Joint Deception Attacks

A dynamic event-triggered distributed model predictive control (DMPC) framework based on the Stackelberg game is proposed to address the stability challenges of nonlinear multiagent systems (MASs) operating under network resource constraints and the threat of deception attacks. To enhance the efficacy of the event-triggering mechanism and optimize communication resource utilization, a dynamic event-triggering mechanism incorporating an adaptive bias component is introduced. Furthermore, to emphasize the dynamic characteristics of deception attacks within network communication, a Stackelberg game framework is proposed to analyze the strategic interactions between deception attacks and control strategies. Additionally, the predictive model information of agents is integrated into the DMPC to mitigate performance degradation resulting from asynchronous triggering in MASs. The theoretical conditions necessary to ensure the actual stability of the input state of the closed-loop system are delineated. Finally, a numerical example is presented to validate the effectiveness and superiority of the proposed algorithm.