Structure-Based Self-Triggered Consensus in Networks of Multiagents with Switching Topologies

TL;DR

This paper introduces a novel self-triggered consensus algorithm for multi-agent networks that relies on coupling structure rather than state observation, with proven convergence and adaptability to switching topologies.

Contribution

It presents a new, easier-to-understand self-triggered consensus algorithm based on coupling structure, applicable to networks with switching topologies, and provides a discrete-time analysis framework.

Findings

Algorithm converges under switching topologies.

Provides explicit bounds for update intervals.

Numerical simulations confirm theoretical results.

Abstract

In this paper, we propose a new self-triggered consensus algorithm in networks of multi-agents. Different from existing works, which are based on the observation of states, here, each agent determines its next update time based on its coupling structure. Both centralized and distributed approaches of the algorithms have been discussed. By transforming the algorithm to a proper discrete-time systems without self delays, we established a new analysis framework to prove the convergence of the algorithm. Then we extended the algorithm to networks with switching topologies, especially stochastically switching topologies. Compared to existing works, our algorithm is easier to understand and implement. It explicitly provides positive lower and upper bounds for the update time interval of each agent based on its coupling structure, which can also be independently adjusted by each agent according to its own situation. Our work reveals that the event/self triggered algorithms are essentially discrete and more suitable to a discrete analysis framework. Numerical simulations are also provided to illustrate the theoretical results.