Open AccessTerminal Iterative Learning Scheme for Consensus Problem in Multi-Agent Systems with State Constraints
Author(s) -
Yan Zhang,
Zhibing Luo,
Wenjun Xiong
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2187/1/012009
Subject(s) - iterative learning control , terminal (telecommunication) , computer science , constraint (computer aided design) , scheme (mathematics) , state (computer science) , multi agent system , consensus , mathematical optimization , tracking error , tracking (education) , state information , convergence (economics) , artificial intelligence , mathematics , algorithm , psychology , telecommunications , mathematical analysis , pedagogy , geometry , control (management) , economics , economic growth
In this paper, we investigate the consensus problem of multi-agent systems with state constraints. To achieve the consensus effectively, the terminal iterative learning approach is proposed. This learning strategy is designed without the tracking error. And the consensus state is obtained by the the information interaction between agents. Meanwhile, the constraint condition holds in terms of our learning strategy. It shows the consensus conditions ensure the achievement of the constraints. Finally, a numerical simulation is given to illustrate the effectiveness of the main results.