Event-driven prescribed performance consensus control for nonlinear MASs with unknown input saturation and control direction

This paper addresses the event-triggered distributed prescribed performance consensus control problem for uncertain nonlinear multi-agent systems subject to unknown input saturation and control direction. To address system uncertainties, we estimate unknown nonlinear functions through the universal approximation capability of neural networks. Furthermore, Nussbaum-type functions are utilized to concurrently handle the unknown saturation and control direction. Considering that the communication resources may be limited, a dynamic event-triggered mechanism is constructed. Unlike existing relative-threshold-based approaches, an adaptive threshold is designed based on consensus error to ensure event-triggered communication. Meanwhile, a prescribed performance framework is employed to ensure the consensus error remains strictly bounded. Building upon these techniques, a distributed event-triggered consensus control strategy with prescribed performance is developed. Its theoretical feasibility is rigorously verified using Lyapunov stability theory, and two simulation examples are finally provided to demonstrate the effectiveness of the proposed strategy.