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

TL;DR

This paper introduces a distributed event-triggered consensus method for linear multi-agent systems that guarantees convergence rate, robustness to uncertainties, and optimal parameter design through convex optimization.

Contribution

It presents a novel approach converting the consensus problem into a stability problem and designs event-triggering thresholds and control gains simultaneously using local convex optimization.

Findings

Effective convergence rate guarantees demonstrated in simulations

Resilience to control gain uncertainties validated

Pareto optimality of design parameters achieved

Abstract

The paper proposes a distributed eventtriggered 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 the control gain are designed simultaneously by solving a convex constrained optimization problem. Followed by some preliminary steps, the optimization 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.