NDLI: Adaptive Neural Network Output Feedback Control for Stochastic Nonlinear Systems With Unknown Dead-Zone and Unmodeled Dynamics

Content Provider	IEEE Xplore Digital Library
Author	Shaocheng Tong Tong Wang Yongming Li Huaguang Zhang
Copyright Year	2013
Abstract	This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.
Page Count	12
File Size	4064951
Starting Page	910
Ending Page	921
File Format	PDF
ISSN	21682267
Volume Number	44
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Output feedback Observers Backstepping Nonlinear dynamical systems Artificial neural networks unmodeled dynamics Backstepping control dead-zone input neural networks small-gain theorem stochastic nonlinear systems
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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