A Game-Theoretical Approach for a Finite-Time Consensus of Second-Order Multi-Agent System
Abstract
The second-order consensus problem depends on not only the topology condition but also the coupling strength of the relative positions and velocities between neighboring agents. This paper seeks to solve the finite-time consensus problem of second-order multi-agent systems by games with special structures. Potential game and weakly acyclic game were applied for modeling the second-order consensus problem with different topologies. Furthermore, this paper introduces the event-triggered asynchronous cellular learning automata algorithm for optimizing the decision making process of the agents, which facilitates a convergence with the Nash equilibrium. Finally, numerical examples illustrate the effectiveness of the models.
Recommended Citation
L. Xue et al., "A Game-Theoretical Approach for a Finite-Time Consensus of Second-Order Multi-Agent System," International Journal of Control, Automation and Systems, vol. 17, no. 5, pp. 1071 - 1083, Institute of Control, Robotics and Systems (ICROS), May 2019.
The definitive version is available at https://doi.org/10.1007/s12555-017-0716-8
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Event-Triggered Asynchronous Cellular Learning Automata; Finite-Time Second-Order Consensus; Graphical Games; Multi-Agent System; Potential Game; Weakly Acyclic Game
International Standard Serial Number (ISSN)
1598-6446; 2005-4092
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Institute of Control, Robotics and Systems (ICROS), All rights reserved.
Publication Date
01 May 2019
Comments
This research was supported by the National Natural Science Foundation of China under grants 61806052, by the Natural Science Foundation of Jiangsu Province of China under grants BK20180361, and by the Fundamental Research Funds for the Central Universities. Partial support for this research was received from the Missouri University of Science and Technology Intelligent Systems Center, the Mary K. Finley Missouri Endowment, the Lifelong Learning Machines program from DARPA/Microsystems Technology Office, and the Army Research Laboratory (ARL); and it was accomplished under Cooperative Agreement Number W911NF-18-2-0260.