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CEASE: A Collaborative Event-Triggered Average-Consensus Sampled-Data Framework With Performance Guarantees for Multi-Agent Systems

The paper proposes a distributed framework for collaborative, event-triggered, average consensus, sampled data (CEASE) algorithms for undirected networked multi-agent systems with two classes of performance guarantees. Referred to as the E-CEASE algorithm, the first approach ensures an exponential rate of convergence and derives associated conditions and optimal design parameters using the Lyapunov stability theorem. The second approach provides a structured tradeoff between the number of transmissions and rate of consensus convergence based on a guaranteed cost and is referred to as the G-CEASE. The distributed implementations of CEASE are event-driven in the sense that agents transmit within their respective neighborhoods only on the triggering of an event. To reduce communication and processing, the triggering condition in CEASE is monitored at discrete-time steps. Monte-Carlo simulations on randomized networks quantify the effectiveness of the proposed approaches.