The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks
Abstract
Because of the complexity of consensus control of nonlinear multiagent systems in state time-delay, most of previous works focused only on linear systems with input time-delay. An adaptive neural network (NN) consensus control method for a class of nonlinear multiagent systems with state time-delay is proposed in this paper. The approximation property of radial basis function neural networks (RBFNNs) is used to neutralize the uncertain nonlinear dynamics in agents. An appropriate Lyapunov-Krasovskii functional, which is obtained from the derivative of an appropriate Lyapunov function, is used to compensate the uncertainties of unknown time delays. It is proved that our proposed approach guarantees the convergence on the basis of Lyapunov stability theory. The simulation results of a nonlinear multiagent time-delay system and a multiple collaborative manipulators system show the effectiveness of the proposed consensus control algorithm.
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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 · cited 2×
“In [10], three consensus problems were explored including fixed topology in directed networks, switching topology in directed networks, and fixed topology with communication time-delay in undirected networks.”
BackgroundConsensus 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 2×
“Lemma 1 [11]: If G = (V , ε, A) is an undirected connected graph, then graph Laplacian matrix L is a symmetric matrix and its n real eigenvalues can be arranged in an ascending order as 0 = λ1 < λ2 ≤ λ3 ≤ · · · ≤ λn ≤ C where C = 2(max1≤i≤n bi ) and λ2 is called the algebraic connectivity and it is used to analyze the…”
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Cited by
Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints
He, W., Huang, H., & Ge, S. S. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2711961 · Cited by 361
Li, T., Li, Z., Wang, D., & Chen, C. L. P. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2334638 · Cited by 256
Self-citationAdaptive Neural Control of Stochastic Nonlinear Time-Delay Systems With Multiple Constraints
Wang, T., Qiu, J., & Gao, H. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2562511 · Cited by 170
Adaptive Intelligent Control of Nonaffine Nonlinear Time-Delay Systems With Dynamic Uncertainties
Wang, H., Sun, W., & Liu, P. X. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2627048 · Cited by 151
Fuzzy adaptive optimal control for nonlinear switched systems with actuator hysteresis
Sui, S., Tong, S., Chen, C. L. P., & Sun, K. (2019) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.2975 · Cited by 23
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Metadata
- Authors
- Chen, C. L. P., Wen, G.-X., Liu, Y.-J., & Wang, F.-Y.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2014-06
- Volume
- 25
- Issue
- 6
- Pages
- 1217-1226
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W1919998456
- Cited by
- 634
- References
- 2
- FWCI
- 70.89
- Type
- journal article
- Language
- en
- Open access
- closed
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