External activation promoting consensus formation in the opinion model with interest decay

Yun Liu; Fei Xiong; Jiang Zhu; Ying Zhang

doi:10.1016/j.physleta.2012.11.044

External activation promoting consensus formation in the opinion model with interest decay

Physics Letters A ◽

10.1016/j.physleta.2012.11.044 ◽

2013 ◽

Vol 377 (5) ◽

pp. 362-366 ◽

Cited By ~ 6

Author(s):

Yun Liu ◽

Fei Xiong ◽

Jiang Zhu ◽

Ying Zhang

Keyword(s):

Consensus Formation ◽

Opinion Model ◽

External Activation

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Leaderless consensus formation control of cooperative multi-agent vehicles without velocity measurements

IEEE Control Systems Letters ◽

10.1109/lcsys.2021.3087095 ◽

2021 ◽

pp. 1-1

Author(s):

Emmanuel Nuno ◽

Antonio Loria ◽

Elena Panteley

Keyword(s):

Formation Control ◽

Velocity Measurements ◽

Consensus Formation ◽

Multi Agent

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Fuzzy Formation Control and Collision Avoidance for Multiagent Systems

Mathematical Problems in Engineering ◽

10.1155/2013/908180 ◽

2013 ◽

Vol 2013 ◽

pp. 1-18 ◽

Cited By ~ 9

Author(s):

Yeong-Hwa Chang ◽

Chun-Lin Chen ◽

Wei-Shou Chan ◽

Hung-Wei Lin ◽

Chia-Wen Chang

Keyword(s):

Collision Avoidance ◽

Multiagent Systems ◽

Formation Control ◽

Descent Method ◽

Minimum Problem ◽

Gradient Descent Method ◽

Consensus Formation ◽

Distributed Information ◽

Graph Theoretic ◽

Learning Rules

This paper aims to investigate the formation control of leader-follower multiagent systems, where the problem of collision avoidance is considered. Based on the graph-theoretic concepts and locally distributed information, a neural fuzzy formation controller is designed with the capability of online learning. The learning rules of controller parameters can be derived from the gradient descent method. To avoid collisions between neighboring agents, a fuzzy separation controller is proposed such that the local minimum problem can be solved. In order to highlight the advantages of this fuzzy logic based collision-free formation control, both of the static and dynamic leaders are discussed for performance comparisons. Simulation results indicate that the proposed fuzzy formation and separation control can provide better formation responses compared to conventional consensus formation and potential-based collision-avoidance algorithms.

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