Adaptive output-feedback control for stochastic nonlinear systems using neural networks

Author

Min Hui-Fang ; Duan Na

Author_Institution

Sch. of Electr. Eng. & Autom., Jiangsu Normal Univ., Xuzhou, China

fYear

2014

fDate

28-30 July 2014

Firstpage

5288

Lastpage

5293

Abstract

This paper considers the output-feedback control problem for a class of stochastic nonlinear systems with unknown control directions and perturbations. By using radial basis function neural network (RBF NN) approximation approach, the tuning function method and backstepping technique, an adaptive output-feedback controller is successfully constructed to guarantee the closed-loop system to be mean square semi-globally uniformly ultimately bounded (M-SGUUB). A simulation example demonstrates the effectiveness of the proposed scheme.

Keywords

adaptive control; closed loop systems; control system synthesis; feedback; neurocontrollers; nonlinear control systems; radial basis function networks; stability; stochastic systems; M-SGUUB; RBFNN approximation approach; adaptive output-feedback control; backstepping technique; closed-loop system; control directions; control perturbation; mean square semi-globally uniformly ultimately bounded system; neural networks; radial basis function neural network; stochastic nonlinear systems; tuning function method; Adaptive systems; Approximation methods; Artificial neural networks; Closed loop systems; Nonlinear systems; Neural Networks; Output-Feedback Control; Stochastic Nonlinear Systems; Tuning Function; Unknown Control Directions;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2014 33rd Chinese

Conference_Location

Nanjing

Type

conf

DOI

10.1109/ChiCC.2014.6895841

Filename

6895841