The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems.
By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed.
The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online.
One example is given to show the effectiveness of the proposed control method.
American Psychological Association (APA)
Qin, Xiaoyan. 2018. The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1207548
Modern Language Association (MLA)
Qin, Xiaoyan. The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems. Mathematical Problems in Engineering No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1207548
American Medical Association (AMA)
Qin, Xiaoyan. The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1207548
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1207548