Distributed Model-Free Adaptive Learning Control of Discrete-Time Nonlinear Multiagent Systems
This article investigates the distributed control problem for nonlinear multiagent systems (MASs) with unknown system models. A novel distributed model-free adaptive learning algorithm is developed to learn a controller from the online system data. Notably, a significant advancement over conventional methods is that the proposed algorithm requires only local interaction data from neighboring agents, eliminating dependencies on both a priori system structural knowledge and global topology information. Comprehensive simulations validate the theoretical results and demonstrate the superior efficacy of the devised algorithm.
Authors
- Shixu Xu
- Zheng‐Guang Wu (ORCID: https://orcid.org/0000-0003-4460-9785)
- Chao Deng (ORCID: https://orcid.org/0000-0002-6148-1034)
- Wei‐Wei Che (ORCID: https://orcid.org/0000-0002-1077-3598)
- Yongsheng Ma (ORCID: https://orcid.org/0000-0001-6541-6830)
Institutions
- Qingdao University (CN)
- Beijing Institute of Technology (CN)
- Nanjing University of Posts and Telecommunications (CN)
- State Key Laboratory of Industrial Control Technology (CN)
- State Key Laboratory of Synthetical Automation for Process Industries
- Northeastern University (CN)
Publication Details
- Journal
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1109/tnnls.2025.3575423
- Citations
- 2
- Primary Topic
- Iterative Learning Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province