Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863982 | Neurocomputing | 2018 | 31 Pages |
Abstract
In this paper, we study the distributed optimization problem of multi-agent systems with delayed sampled-data, where the interconnected topology is directed, weighted-balanced and strongly connected, and also local cost functions are strongly convex with globally Lipschitz gradients. Based on synchronous and asynchronous sampled-data, we construct two respective algorithms. Our main results, sufficient conditions for the convergence to an optimal solution, are obtained under assumption that all design parameters are chosen properly. We also present one example to validate our theoretical results.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Junxiu Yan, Hui Yu, Xiaohua Xia,