Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 62, Issue 7, Pages 3602-3609Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2017.2669321
Keywords
Adaptive approach; convex optimization; distributed optimization; linear system; multiagent system
Funding
- National Natural Science Foundation of China [61603300, 61603301, 61673104]
- Fundamental Research Funds for the Central Universities of China [G2016KY0101, 2242016K41030]
- Chung-Ying Tang Foundation
- Hong Kong Research Grants Council under the GRF [CityU 11234916]
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This paper studies the distributed optimization problem for continuous-time multiagent systems with general linear dynamics. The objective is to cooperatively optimize a team performance function formed by a sum of convex local objective functions. Each agent utilizes only local interaction and the gradient of its own local objective function. To achieve the cooperative goal, a couple of fully distributed optimal algorithms are designed. First, an edge-based adaptive algorithm is developed for linear multiagent systems with a class of convex local objective functions. Then, a node-based adaptive algorithm is constructed to solve the distributed optimization problem for a class of agents satisfying the bounded-input bounded-state stable property. Sufficient conditions are given to ensure that all agents reach a consensus while minimizing the team performance function. Finally, numerical examples are provided to illustrate the theoretical results.
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