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Distributed Optimization for Linear Multiagent Systems: Edge- and Node-Based Adaptive Designs
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DOI：10.1109/TAC.2017.2669321.png)
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Abstract
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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.
Keywords:
Adaptive approach
convex optimization
distributed optimization
linear system
multiagent system
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