Adaptive output feedback tracking control of stochastic nonlinear systems with dynamic uncertainties. (27th January 2014)

• Record Type: Journal Article
• Authors: Zhang, Tianping; Xia, Xiaonan
• Is Part Of: International journal of robust and nonlinear control. Volume 25:Number 9(2015)
• Page Start: 1282
• Page End: 1300
• Publication Date: 2014-01-27
• Subjects: Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.3139
• Languages: English
• ISSNs: 1049-8923

Abstract

<abstract abstract-type="main" id="rnc3139-abs-0001"> <title>Summary</title> <p id="rnc3139-para-0001">In this paper, adaptive output feedback tracking control is developed for a class of stochastic nonlinear systems with dynamic uncertainties and unmeasured states. Neural networks are used to approximate the unknown nonlinear functions. K‐filters are designed to estimate the unmeasured states. An available dynamic signal is introduced to dominate the unmodeled dynamics. By combining dynamic surface control technique with backstepping, the condition in which the approximation error is assumed to be bounded is avoided. Using It <italic>ô</italic> formula and Chebyshev's inequality, it is shown that all signals in the closed‐loop system are bounded in probability, and the error signals are semi‐globally uniformly ultimately bounded in mean square or the sense of four‐moment. Simulation results are provided to illustrate the effectiveness of the proposed approach. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>