Adaptive neural tracking control for a class of stochastic nonlinear systems

• Published in: International journal of robust and nonlinear control Vol. 24; no. 7; pp. 1262 - 1280
• Main Authors: Wang, Huan-qing, Chen, Bing, Lin, Chong
• Format: Journal Article
• Language: English
• Published: Bognor Regis Blackwell Publishing Ltd 10.05.2014; Wiley Subscription Services, Inc
• Subjects: Adaptive control systems; adaptive tracking control; backstepping design; Controllers; Dynamical systems; Mathematical models; Neural networks; Nonlinear dynamics; Nonlinearity; stochastic nonlinear systems; Stochasticity; the neural networks control
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.2943

SUMMARYThis 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 & Sons, Ltd.