Self-tuning dynamic event-triggered consensus control for multi-agent systems and its implementation via...

This paper addresses the event-triggered consensus control of general linear multi-agent systems. A novel self-tuning dynamic event-triggered control is first proposed, which effectively reduces the triggering frequency while allowing the convergence performance to be improved through appropriate parameter tuning. The parameters in Young's inequality are treated as design variables in the triggering mechanisms, providing flexibility to balance convergence speed and triggering behavior. To avoid continuous monitoring of agents' states, a self-tuning dynamic self-triggering mechanism is then developed. Additionally, the proposed triggering mechanisms eliminate the need for simultaneous measurement of neighbors. Using Lyapunov-based analysis, sufficient conditions are derived to guarantee asymptotic consensus and the exclusion of Zeno behavior. Both theoretical and simulation results demonstrate the stability of the multi-agent system. Comparisons with state-of-the-art methods highlight the improvements of our proposed methods in terms of faster convergence rates and fewer triggers.