Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties
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 dy...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 32; no. 5; pp. 2239 - 2250 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-237X 2162-2388 2162-2388 |
| DOI | 10.1109/TNNLS.2020.3003950 |
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| 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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| AbstractList | 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. 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.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. |
| Author | Liang, Hongjing Huang, Tingwen Liu, Guangliang Zhang, Huaguang |
| Author_xml | – sequence: 1 givenname: Hongjing orcidid: 0000-0003-1480-1872 surname: Liang fullname: Liang, Hongjing email: lianghongjing99@163.com organization: College of Engineering, Bohai University, Jinzhou, China – sequence: 2 givenname: Guangliang orcidid: 0000-0002-6385-0497 surname: Liu fullname: Liu, Guangliang email: liuguangliang17@163.com organization: College of Engineering, Bohai University, Jinzhou, China – sequence: 3 givenname: Huaguang orcidid: 0000-0002-0647-4050 surname: Zhang fullname: Zhang, Huaguang email: hgzhang@ieee.org organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China – sequence: 4 givenname: Tingwen orcidid: 0000-0001-9610-846X surname: Huang fullname: Huang, Tingwen email: tingwen.huang@qatar.tamu.edu organization: Science Program, Texas A&M University at Qatar, Doha, Qatar |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32663131$$D View this record in MEDLINE/PubMed |
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| CODEN | ITNNAL |
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| SubjectTerms | Adaptive control Adaptive event-triggered control Algorithms Control systems Feedback control Liapunov functions Multi-agent systems Multiagent systems Neural networks neural networks (NNs) nonaffine multiagent systems Nonlinear control Nonlinear systems Protocols Radial basis function Robustness Uncertainty unmodeled dynamics Vehicle dynamics |
| Title | Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties |
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