The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Neural‐network‐based adaptive leader‐following consensus control for second‐order non‐linear multi‐agent systems
Abstract
In this study, a novel adaptive neural network (NN)‐based leader‐following consensus approach is proposed for a class of non‐linear second‐order multi‐agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN‐based adaptive consensus algorithms require the number of NN nodes to must be large enough, and thus the online computation burden often are very heavy. However, the proposed adaptive consensus scheme can greatly reduce the online computation burden, because the adaptive adjusting parameters are designed in scalar form, which is the norm of the estimation of the optimal NN weight matrix. According to Lyapunov stability theory, the proposed approach can guarantee the leader‐following consensus behaviour of non‐linear second‐order multi‐agent systems to be obtained. Finally, a numerical simulation and a multi‐manipulator simulation are carried out to further demonstrate the effectiveness of the proposed consensus approach.
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Cites
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788
Consensus and Cooperation in Networked Multi-Agent Systems
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.887293 · Cited by 10,522 · cited 1×
“Usually, consensus control has two control strategies [8–11], the leaderless and the leader-following consensus.”
BackgroundCoordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., & Morse, A. S. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.812781 · Cited by 8,339
Cited by
Liu, Y.-J., Li, J., Tong, S., & Chen, C. L. P. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2508926 · Cited by 517
Self-citationAdaptive Neural Network Control for a Class of Nonlinear Systems With Function Constraints on States
Liu, Y.-J., Zhao, W., Liu, L., Li, D., Tong, S., & Chen, C. L. P. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3107600 · Cited by 331
Self-citationAdaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems
Liu, Y.-J., Lu, S., Li, D., & Tong, S. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2633007 · Cited by 284
Self-citationLi, D.-P., Li, D.-J., Liu, Y.-J., Tong, S., & Chen, C. L. P. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2707178 · Cited by 148
Self-citationNi, J., Wu, Z., Liu, L., & Liu, C. (2020) · ISA Transactions · doi:10.1016/j.isatra.2019.07.013 · Cited by 105
Metadata
- Authors
- Wen, G., Chen, C. L. P., Liu, Y., & Liu, Z.
- Venue
- IET Control Theory & Applications
- Published
- 2015-08
- Volume
- 9
- Issue
- 13
- Pages
- 1927-1934
- ISSN
- 1751-8644 (ISSN-L)
- 1751-8652
- OpenAlex
- W2262108890
- Cited by
- 250
- References
- 3
- FWCI
- 41.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wen, G., Chen, C. L. P., Liu, Y., & Liu, Z. (2015). Neural‐network‐based adaptive leader‐following consensus control for second‐order non‐linear multi‐agent systems. IET Control Theory & Applications. https://doi.org/10.1049/iet-cta.2014.1319
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