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Sampled-data based distributed convex optimization with event-triggered communication

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  • Control Theory and Applications
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Abstract

This paper studies the distributed convex optimization problem for multi-agent systems over undirected and connected networks. Motivated by practical considerations, we propose a new distributed optimization algorithm with event-triggered communication. The proposed event detection is decentralized, sampled-data and not requires periodic communications among agents to calculate the threshold. Based on Lyapunov approaches, we show that the proposed algorithm is asymptotically converge to the unknown optimizer if the design parameters are chosen properly. We also give an upper bound on the convergence rate. Finally, we illustrate the effectiveness of the proposed algorithm by a numerical simulation.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xian, 710071, China

    Jiayun Liu

  2. School of Aerospace Science and Technology, Xidian University, Xian, 710071, China

    Weisheng Chen & Hao Dai

Authors
  1. Jiayun Liu
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  2. Weisheng Chen
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  3. Hao Dai
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Corresponding author

Correspondence to Weisheng Chen.

Additional information

Recommended by Associate Editor Hongbo Li under the direction of Editor Euntai Kim. This work was supported by National Natural Science Foundation of China (61673308, 61503292).

Jiayun Liu received her B.Sc. and M.Sc degrees from Shandong Normal University, Jinan, China, in 2000 and 2003, respectively. She is currently working toward the Ph.D. degree with the Department of Mathematics, Xidian University. Her current research interests include event-triggered control, distributed optimization and consensus.

Weisheng Chen received his B.Sc. degree from the Department of Mathematics, Qufu Normal University, Qufu, China, in 2000, and his M.Sc. and Ph.D. degrees from the Department of Applied Mathematics, Xidian University, Xi’an, China, in 2004 and 2007, respectively. He was a Visiting Scholar with the Automation School, Southeast University, Nanjing, China, from 2008 to 2009 and the Department of Electrical Engineering, University of California, Riverside, CA, USA, from 2013 to 2014. He is currently a Professor with the School of Mathematics and Statistics, Xidian University. His current research interests include multi-agent systems, nonlinear systems, adaptive control, event-triggered control and distributed machine learning.

Hao Dai received his B.S. degree from Xidian University of Information and Computing Science, Xi’an, China, in 2008, his M.S. and Ph.D. degrees from Xi’an Jiaotong University of Control Science and Engineering and Electrical Engineering, Xi’an, China, in 2011 and 2014, respectively. He is currently a lecturer in the School of Aerospace Science and Technology, Xidian University, Xi’an, China. His current research interests include complex network, nonlinear systems, distributed learning and adaptive control.

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Liu, J., Chen, W. & Dai, H. Sampled-data based distributed convex optimization with event-triggered communication. Int. J. Control Autom. Syst. 14, 1421–1429 (2016). https://doi.org/10.1007/s12555-015-0133-9

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  • DOI: https://doi.org/10.1007/s12555-015-0133-9

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