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Adaptive fixed-time bipartite tracking consensus control for unknown nonlinear multi-agent systems: An information classification mechanism
Abstract
Abstract This paper is concerned with the problem of bipartite tracking consensus for high-order unknown nonlinear multi-agent systems with actuator faults. Unlike the traditional condition that the directed signed graph is structurally balanced, a directed signed graph containing a spanning tree is considered. Besides, the consensus errors are required to satisfy both the prescribed performance and fast convergence (fixed-time). By proposing an information classification mechanism, each agent selectively uses neighbor information such that agents in the system are divided into two styles, which transform the bipartite tracking consensus problem into a general tracking consensus problem. By using neural networks and adaptive technologies to approximate unknown functions, the adaptive fault-tolerant fixed-time consensus controllers are developed. All signals in the system are bounded within a fixed time. Moreover, the bipartite consensus errors satisfy the prescribed performance by selecting appropriately predefined performance functions. Stability analysis and simulation results further verify the effectiveness of the proposed method.
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Citations
Cited by
Sun, J., Yi, J., & Pu, Z. (2022) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3052610 · Cited by 184
Ni, J., Wu, Z., Liu, L., & Liu, C. (2020) · ISA Transactions · doi:10.1016/j.isatra.2019.07.013 · Cited by 105
Jiao, J., Li, J., Chen, J., & Zhang, R. (2026) · Asian Journal of Control · doi:10.1002/asjc.3705 · Cited by 2
Metadata
- Authors
- Yang, H., & Ye, D.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 459
- Pages
- 238-254
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2796225979
- Cited by
- 279
- References
- 0
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
