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Consensus seeking of multi-agent systems from an iterative learning perspective

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  • Control Theory and Applications
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Abstract

The consensus seeking problems for both discrete and continuous multi-agent networks are discussed from an iterative learning perspective. It is shown that the consensus seeking process can be viewed as an iterative learning process for agents under directed networks to improve their performances from time to time in order to achieve consensus. If a desired consensus state is specified, then the multi-agent system can be guaranteed to reach consensus through reducing the tracking error between each agent’s state and the desired consensus state monotonically to zero with respect to the increasing of time. If there is no desired consensus state, then the agents can achieve consensus through reducing their states monotonically to the minimum quantity with increasing time. Simulations illustrate the observed results.

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Authors and Affiliations

  1. Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China

    Juntao Li & Yadi Wang

  2. School of Mathematics and Information Science, Henan University of Economics and Law, Zhengzhou, 450002, China

    Huimin Xiao

Authors
  1. Juntao Li
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  2. Yadi Wang
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  3. Huimin Xiao
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Corresponding author

Correspondence to Juntao Li.

Additional information

Recommended by Associate Editor Young Ik Son under the direction of Editor Euntai Kim. This work was supported by National Natural Science Foundation of China (61374079, 61203293, 61473010), Key Scientific and Technological Project of Henan Province (122102210131), Program for Science and Technology Innovation Talents in Universities of Henan Province (13HASTIT040), Henan Higher School Funding Scheme for Young Teachers (2012GGJS-063), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (14IRTSTHN023).

Juntao Li received his B.S. and M.S. degrees in Applied Mathematics from Henan Normal University, P.R. China, in 2001 and 2004, respectively. He received his Ph.D. degree in Control Theory and Control Engineering from Beihang University, P.R. China, in 2010. Since 2011, he is an associate professor of Henan Normal University. His research interests include statistical machine learning, bioinformatics and complex system modeling.

Yadi Wang received bachelor degree in Mathematics and Information Science from Henan Polytechnic University in 2012, and now she is a Master degree candidate in School of Mathematics and Information Science at Henan Normal University. Her research interests include control theory and statistical machine learning.

Huimin Xiao is a professor at the School of Mathematics and Information Science, Henan University of Economics and Law. He received his M.S. degree in Operation Research and Control Theory from Huazhong Normal University in 1988, and his Ph.D. degree in Automation from South China University of Technology in 1991. His research interests include the complex systems modeling, control and analysis, management information systems, etc.

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Li, J., Wang, Y. & Xiao, H. Consensus seeking of multi-agent systems from an iterative learning perspective. Int. J. Control Autom. Syst. 14, 1173–1182 (2016). https://doi.org/10.1007/s12555-015-0103-2

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  • DOI: https://doi.org/10.1007/s12555-015-0103-2

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