Adaptive Neural Control of Uncertain Nonstrict-Feedback Stochastic Nonlinear Systems with Output Constraint and Unknown Dead Zone

被引:236
|
作者
Li, Hongyi [1 ,2 ]
Bai, Lu [3 ]
Wang, Lijie [3 ]
Zhou, Qi [4 ,5 ]
Wang, Huanqing
机构
[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
[2] Bohai Univ, Coll Engn, Jinzhou 121013, Peoples R China
[3] Bohai Univ, Sch Math & Phys, Jinzhou 121013, Peoples R China
[4] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
[5] Coll Informat Sci & Technol, Jinzhou 121013, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive neural control; backstepping; nonstrict-feedback system; output constraint; DYNAMIC SURFACE CONTROL; TRACKING CONTROL; DELAY SYSTEMS; STABILIZATION; DESIGN; CRANE;
D O I
10.1109/TSMC.2016.2605706
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An approximation-based adaptive neural controller is constructed for uncertain stochastic nonlinear systems in nonstrict-feedback form appearing dead-zone and output constraint. Neural networks (NNs) are directly utilized to approximate the unknown nonlinear functions existing in systems. A barrier Lyapunov function is introduced to ensure that the trajectory of output is limited within a predetermined range. By integrating NNs into the backstepping technique, an adaptive neural controller is designed to guarantee all variables existing in the considered closed-loop system are semi-globally uniformly ultimately bounded, and by appropriately tuning several design parameters online, the tracking error can be converged to a small neighborhood of the origin. Simulations on a numerical example are given to demonstrate the effectiveness of the method proposed in this paper.
引用
收藏
页码:2048 / 2059
页数:12
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