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arXiv · 1803.06380

Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication

Xinlei Yi·Lisha Yao·Tao Yang·Jemin George·Karl H. Johansson
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Abstract

In this paper, we propose a fully distributed algorithm for second-order continuous-time multi-agent systems to solve the distributed optimization problem. The global objective function is a sum of private cost functions associated with the individual agents and the interaction between agents is described by a weighted undirected graph. We show the exponential convergence of the proposed algorithm if the underlying graph is connected, each private cost function is locally gradient-Lipschitz-continuous, and the global objective function is restricted strongly convex with respect to the global minimizer. Moreover, to reduce the overall need of communication, we then propose a dynamic event-triggered communication mechanism that is free of Zeno behavior. It is shown that the exponential convergence is achieved if the private cost functions are also globally gradient-Lipschitz-continuous. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

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Xinlei Yi, Lisha Yao, Tao Yang, Jemin George, Karl H. Johansson. 2018-03-16. Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication. https://doi.org/10.1109/cdc.2018.8618989

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