Data-Driven-Based Bipartite Tracking Consensus Control for Networked Agent Systems via Adaptive Policy Iteration
In this paper, the optimal bipartite tracking consensus control problem of networked agent systems is addressed. Theoretically, a data-driven control scheme based on adaptive policy iteration (API) is designed to enable all followers in a cooperative-competitive network to achieve bipartite tracking for the leader. Firstly, the Hamilton-Jacobi-Bellman (HJB) equation of this optimal tracking consensus control problem is obtained based on the Bellman optimality principle. Secondly, an API algorithm with the adaptive escape mechanism is designed to achieve the solution of the HJB equation while reducing the iteration resource consumption. Then, the optimal value function and optimal control input are solved online based on neural networks. Finally, a simulation example is given to verify the effectiveness of the proposed data-driven-based bipartite tracking consensus control scheme.
Authors
- Weihao Li (ORCID: https://orcid.org/0000-0002-9447-7023)
- Jiangfeng Yue (ORCID: https://orcid.org/0009-0007-0922-2303)
- Mengji Shi (ORCID: https://orcid.org/0000-0003-1360-9846)
- Boxian Lin (ORCID: https://orcid.org/0000-0001-9481-2883)
- Ming‐Bo Wang (ORCID: https://orcid.org/0000-0002-3979-3103)
- Kaiyu Qin (ORCID: https://orcid.org/0000-0002-0812-7095)
- Peiyu Zhai
Institutions
- University of Electronic Science and Technology of China (CN)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Published
- 2024-11-01
- DOI
- https://doi.org/10.1109/cac63892.2024.10865535
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities