Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems
Due to the excellent approximation ability, the neural networks based control method is used to achieve adaptive consensus of the fractional-order uncertain nonlinear multiagent systems with external disturbance. The unknown nonlinear term and the external disturbance term in the systems are compens...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/9014787 |
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| _version_ | 1832548444733964288 |
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| author | Jing Bai Yongguang Yu |
| author_facet | Jing Bai Yongguang Yu |
| author_sort | Jing Bai |
| collection | DOAJ |
| description | Due to the excellent approximation ability, the neural networks based control method is used to achieve adaptive consensus of the fractional-order uncertain nonlinear multiagent systems with external disturbance. The unknown nonlinear term and the external disturbance term in the systems are compensated by using the radial basis function neural networks method, a corresponding fractional-order adaption law is designed to approach the ideal neural network weight matrix of the unknown nonlinear terms, and a control law is designed eventually. According to the designed Lyapunov candidate function and the fractional theory, the systems stability is proved, and the adaptive consensus can be guaranteed by using the designed control law. Finally, two simulations are shown to illustrate the validity of the obtained results. |
| format | Article |
| id | doaj-art-ff3274be9b75489e9ba2e15638b6ff56 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ff3274be9b75489e9ba2e15638b6ff562025-02-03T06:14:12ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/90147879014787Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent SystemsJing Bai0Yongguang Yu1School of Mathematics and Physics, University of Science and Technology, Beijing 100083, ChinaDepartment of Mathematics, Beijing Jiaotong University, Beijing 100044, ChinaDue to the excellent approximation ability, the neural networks based control method is used to achieve adaptive consensus of the fractional-order uncertain nonlinear multiagent systems with external disturbance. The unknown nonlinear term and the external disturbance term in the systems are compensated by using the radial basis function neural networks method, a corresponding fractional-order adaption law is designed to approach the ideal neural network weight matrix of the unknown nonlinear terms, and a control law is designed eventually. According to the designed Lyapunov candidate function and the fractional theory, the systems stability is proved, and the adaptive consensus can be guaranteed by using the designed control law. Finally, two simulations are shown to illustrate the validity of the obtained results.http://dx.doi.org/10.1155/2018/9014787 |
| spellingShingle | Jing Bai Yongguang Yu Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems Complexity |
| title | Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems |
| title_full | Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems |
| title_fullStr | Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems |
| title_full_unstemmed | Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems |
| title_short | Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems |
| title_sort | neural networks based adaptive consensus for a class of fractional order uncertain nonlinear multiagent systems |
| url | http://dx.doi.org/10.1155/2018/9014787 |
| work_keys_str_mv | AT jingbai neuralnetworksbasedadaptiveconsensusforaclassoffractionalorderuncertainnonlinearmultiagentsystems AT yongguangyu neuralnetworksbasedadaptiveconsensusforaclassoffractionalorderuncertainnonlinearmultiagentsystems |