The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties
Abstract
This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader's output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.
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Citations
Cites
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×
“I N RECENT years, the consensus problem for multiagent systems has attracted great interests due to its broad applications in many areas, such as unmanned air vehicle formations [1], triggering networks [2], and cooperative surveillance [3].”
BackgroundDistributed event-triggered control for multi-agent systems
Dimarogonas, D. V., Frazzoli, E., & Johansson, K. H. (2012) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2174666 · Cited by 2,556
Distributed event-triggered control of multi-agent systems with combinational measurements
Fan, Y., Feng, G., Wang, Y., & Song, C. (2013) · Automatica · doi:10.1016/j.automatica.2012.11.010 · Cited by 799
Guo, G., Ding, L., & Han, Q.-L. (2014) · Automatica · doi:10.1016/j.automatica.2014.03.017 · Cited by 720
Chen, C. L. P., Wen, G.-X., Liu, Y.-J., & Wang, F.-Y. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2302477 · Cited by 634
Sun, Q., Han, R., Zhang, H., Zhou, J., & Guerrero, J. M. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2412779 · Cited by 401
Self-citationAdaptive compensation for actuator failures with event-triggered input
Xing, L., Wen, C., Liu, Z., Su, H., & Cai, J. (2017) · Automatica · doi:10.1016/j.automatica.2017.07.061 · Cited by 393
Liang, H., Guo, X., Pan, Y., & Huang, T. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2982618 · Cited by 379
Self-citationAdaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone
Zhou, Q. F., Zhao, S., Li, H., Lu, R., & Wu, C. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2869375 · Cited by 378
Adaptive Tracking Control for Robots with Unknown Kinematic and Dynamic Properties
Cheah, C. C., Liu, C., & Slotine, J. J. E. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906063830 · Cited by 313
Cited by
An Improved Predefined-Time Adaptive Neural Control Approach for Nonlinear Multiagent Systems
Pan, Y., Ji, W., Lam, H.-K., & Cao, L. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3324397 · Cited by 294
Metadata
- Authors
- Liang, H., Liu, G., Zhang, H., & Huang, T.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2021-05
- Volume
- 32
- Issue
- 5
- Pages
- 2239-2250
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W3043661245
- Cited by
- 517
- References
- 27
- FWCI
- 45.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liang, H., Liu, G., Zhang, H., & Huang, T. (2021). Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2020.3003950
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