Distributed Optimization for Linear Multiagent Systems : Edge- and Node-Based Adaptive Designs

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

113 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Article number7857020
Pages (from-to)3602-3609
Journal / PublicationIEEE Transactions on Automatic Control
Issue number7
Online published15 Feb 2017
Publication statusPublished - Jul 2017


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 edgebased
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.

Research Area(s)

  • Adaptive approach, convex optimization, distributed optimization, linear system, multiagent system