Performance Constrained Distributed Event-triggered Consensus in Multi-agent Systems
For multi-agent systems, this work provides a method to simultaneously optimize event-triggering threshold and control gain under performance constraints, but it is incremental as it builds on existing Lyapunov-based techniques.
The paper proposes a distributed event-triggered consensus method for linear multi-agent systems that guarantees rate of convergence, resilience to control gain uncertainties, and Pareto optimality of design parameters. The approach is validated through simulations on an experimental multi-agent system.
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.