Consensus analysis of systems with time-varying interactions : An event-triggered approach
It addresses control effort reduction in multi-agent consensus for systems with time-varying interactions, but the results are incremental as they extend existing event-triggered control to specific graph conditions.
This paper studies consensus in multi-agent systems with single integrator dynamics under event-triggered control, showing that static triggers lead to bounded convergence while dynamic triggers achieve exponential consensus, with extensions to switching topologies.
We present consensus analysis of systems with single integrator dynamics interacting via time-varying graphs under the event-triggered control paradigm. Event-triggered control sparsifies the control applied, thus reducing the control effort expended. Initially, we consider a multi-agent system with persistently exciting interactions and study the behaviour under the application of event-triggered control with two types of trigger functions- static and dynamic trigger.We show that while in the case of static trigger, the edge-states converge to a ball around the origin, the dynamic trigger function forces the states to reach consensus exponentially. Finally, we extend these results to a more general setting where we consider switching topologies. We show that similar results can be obtained for agents interacting via switching topologies and validate our results by means of simulations.