Performance Constrained Distributed Event-triggered Consensus in Multi-agent Systems

The paper proposes a distributed event-triggered consensus approach for linear multi-agent systems with guarantees over rate of convergence, resilience to control gain uncertainties, and Pareto optimality of design parameters, namely, the event-triggering threshold (ET) and control gain. The event-triggered consensus problem is first converted to stability problem of an equivalent system. The Lyapunov stability theorem is then used to incorporate the performance constraints with the event-triggered consensus. Using an approximated linear scalarization method, the ET and control gain are designed simultaneously from a convex constrained optimization. The optimization step can be performed locally, i.e., no global information is required. The effectiveness of the proposed approach is studied through simulations for an experimental multi-agent system.