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NN‐Based Output‐Feedback Control for Stochastic Nonlinear Systems with Unknown Control Directions
Author(s) -
Min HuiFang,
Duan Na
Publication year - 2016
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1308
Subject(s) - control theory (sociology) , nonlinear system , bounded function , artificial neural network , controller (irrigation) , transformation (genetics) , control (management) , computer science , backstepping , radial basis function , adaptive control , mathematics , artificial intelligence , mathematical analysis , physics , biochemistry , chemistry , quantum mechanics , gene , agronomy , biology
Abstract This paper addresses the neural network‐based output‐feedback control problem for a class of stochastic nonlinear systems with unknown control directions. The restrictions on the drift and diffusion terms are removed and the conditions on unknown control directions are relaxed. By introducing a proper coordinate transformation, and combining dynamic surface control (DSC) technique with radial basis function neural network (RBF NN) approximation approach, we construct an adaptive output‐feedback controller 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.