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Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-12
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Bai, Jing& Yu, Yongguang. 2018. Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems. Complexity،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1136459
Modern Language Association (MLA)
Bai, Jing& Yu, Yongguang. Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems. Complexity No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1136459
American Medical Association (AMA)
Bai, Jing& Yu, Yongguang. Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems. Complexity. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1136459
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136459