Adaptive neural tracking control for a class of stochastic nonlinear systems. (14th December 2012)

• Record Type: Journal Article
• Authors: Wang, Huan‐qing; Chen, Bing; Lin, Chong
• Is Part Of: International journal of robust and nonlinear control. Volume 24:Number 7(2014)
• Page Start: 1262
• Page End: 1280
• Publication Date: 2012-12-14
• Subjects: Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.2943
• Languages: English
• ISSNs: 1049-8923

Abstract

<abstract abstract-type="main" id="rnc2943-abs-0001"> <title>SUMMARY</title> <p id="rnc2943-para-0001">This paper investigates the problem of adaptive neural control design for a class of single‐input single‐output strict‐feedback stochastic nonlinear systems whose output is an known linear function. The radial basis function neural networks are used to approximate the nonlinearities, and adaptive backstepping technique is employed to construct controllers. It is shown that the proposed controller ensures that all signals of the closed‐loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. The salient property of the proposed scheme is that only one adaptive parameter is needed to be tuned online. So, the computational burden is considerably alleviated. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed approach. Copyright © 2012 John Wiley &amp; Sons, Ltd.</p> </abstract>