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Distributed Optimization on Marix-Weighted Networks
  • +1
  • yan Cui,
  • Zhijian Ji,
  • Yungang Liu,
  • Chong Lin
yan Cui
Qingdao University
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Zhijian Ji
Qingdao University

Corresponding Author:[email protected]

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Yungang Liu
Shandong University
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Chong Lin
Qingdao University
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Abstract

This article aims to address the optimization problems for continuous-time first-order and second-order multi-agent systems (MASs) with matrix-weighted networks. The matrix-weighted network is used to model the interdependence between agents’ multidimensional states, providing an effective approach to analyzing complex systems. The goal of optimization is that agents exponentially converge to the optimal value of the global cost function, which is formed by a sum of local cost functions. To achieve this goal, distributed optimization algorithms based on Hessian matrix and gradient information are constructed. Additionally, the edge-based event-triggered mechanism is utilized to avoid communicating with all neighbors at the time of event triggering while theoretically excluding Zeno behavior. The results show that the proposed algorithm can ensure that the intelligent body can achieve the optimization goal while reducing energy consumption. Eventually, an application is presented to substantiate the theoretical results.
07 May 2024Submitted to Optimal Control, Applications and Methods
08 May 2024Submission Checks Completed
08 May 2024Assigned to Editor
12 Jun 2024Review(s) Completed, Editorial Evaluation Pending
12 Jul 2024Editorial Decision: Revise Minor
30 Jul 20241st Revision Received
08 Aug 2024Submission Checks Completed
08 Aug 2024Assigned to Editor
08 Aug 2024Review(s) Completed, Editorial Evaluation Pending
20 Aug 2024Reviewer(s) Assigned
23 Sep 2024Editorial Decision: Accept