Adaptive Neural Network Control of Stochastic Strict Feedback Nonlinear Systems

被引：0

作者：

Wang Fei ^{[1
]}

Zhang Tianping ^{[1
]}

Shi Xiaocheng ^{[1
]}

机构：

[1] Yangzhou Univ, Dept Automat, Coll Informat Engn, Yangzhou 225127, Peoples R China

来源：

2011 30TH CHINESE CONTROL CONFERENCE (CCC) | 2011年

关键词：

Stochastic Nonlinear Systems; Adaptive Control; Neural Networks; Backstepping; Virtual Control Gain Function; UNKNOWN COVARIANCE; STABILIZATION; DESIGN; STABILITY; TRACKING; NOISE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Lyapunov function of integral type is first introduced into a class of stochastic strict-feedback nonlinear systems with unknown virtual control gain functions. By utilizing the approximation capability of neural networks, backstepping technique and Young's inequality, a simple and effective adaptive neural network state feedback controller is constructed to ensure that the system is semi-global bounded in probability. Under some conditions, by the Lyapunov method, it is shown that all signals in the closed-loop system are bounded in probability. Simulation results are given to illustrate the effectiveness of the proposed control scheme.

引用

页码：1306 / 1311

页数：6

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