Back to Search
Start Over
Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems
- Source :
- IEEE Transactions on Neural Networks and Learning Systems; January 2024, Vol. 35 Issue: 1 p417-427, 11p
- Publication Year :
- 2024
-
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.
Details
- Language :
- English
- ISSN :
- 2162237x and 21622388
- Volume :
- 35
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Publication Type :
- Periodical
- Accession number :
- ejs65168148
- Full Text :
- https://doi.org/10.1109/TNNLS.2022.3174885