Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

Zhao, Huarong; Yu, Hongnian; Peng, Li

Abstract

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent’s time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach’s effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

Citation

Zhao, H., Yu, H., & Peng, L. (2024). Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 417-427. https://doi.org/10.1109/tnnls.2022.3174885

• Journal Article Type: Article
• Acceptance Date: Jun 10, 2022
• Online Publication Date: Jun 8, 2022
• Publication Date: 2024
• Deposit Date: Jun 15, 2022
• Publicly Available Date: Jun 16, 2022
• Journal: IEEE Transactions on Neural Networks and Learning Systems
• Print ISSN: 2162-237X
• Electronic ISSN: 2162-2388
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 35
• Issue: 1
• Pages: 417-427
• DOI: https://doi.org/10.1109/tnnls.2022.3174885
• Keywords: Artificial Intelligence; Computer Networks and Communications; Computer Science Applications; Software
• Public URL: http://researchrepository.napier.ac.uk/Output/2879057