The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

Author

Qin, Xiaoyan

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

Civil Engineering

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