Open AccessNeural network‐based output‐feedback control for stochastic high‐order non‐linear time‐delay systems with application to robot system
Author(s) -
Min Huifang,
Lu Junwei,
Xu Shengyuan,
Duan Na,
Chen Weimin
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.1139
Subject(s) - control theory (sociology) , artificial neural network , controller (irrigation) , computer science , moment (physics) , bounded function , stochastic neural network , adaptive control , mathematics , control (management) , recurrent neural network , artificial intelligence , mathematical analysis , physics , classical mechanics , agronomy , biology
This study is concerned with the output‐feedback control problem for a class of stochastic high‐order non‐linear systems with time‐varying delays. A distinctive feature of the control scheme is that the restrictions on delay‐dependent drift and diffusion terms are greatly relaxed by using radial basis function neural network (NN) approximation approach. Furthermore, with the approach, the specific knowledge of NN nodes and weights is not required. Under some weaker conditions, by combining dynamic surface control technique with proper Lyapunov–Krasovskii functional, an adaptive NN output‐feedback controller is designed constructively such that the closed‐loop system is 4‐moment (or mean square) semi‐globally uniformly ultimately bounded. Finally, the control scheme is applied to both a practical stochastic robot system and a numerical system to demonstrate the effectiveness of the proposed approach.