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

Zhao, Huarong; Yu, Hongnian; Peng, Li

Authors

Huarong Zhao

Li Peng



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.

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

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