Global asymptotic adaptive neural control of uncertain nonlinear systems

被引：0
作者：
Luo, Long [1 ,2 ]
Luo, Fei [1 ]
Xu, Yu-Ge [1 ]
机构：
[1] Key Laboratory of Autonomous Systems and Network Control, College of Automation Science and Technology, South China University of Technology, Guangzhou , 510640, Guangdong
[2] Guangzhou Institute of Railway Technology, Guangzhou, 510430, Guangdong
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2014年 / 31卷 / 09期
关键词：
Adaptive control; Asymptotic tracking; Global stability; Neural network; Nonlinear system;
D O I：
10.7641/CTA.2014.31361
中图分类号：
学科分类号：
摘要：
We present an adaptive neural control (ANC) strategy that guarantees globally asymptotic tracking for a class of uncertain nonlinear systems with function-type control gains. A proportion differential (PD) control term with variable gain is employed to globally stabilize the plant so that neural network approximation is applicable. A state transformation is applied to solve the control singularity problem resulting from the unknown control gain function. A robust control term is developed to achieve asymptotic tracking of the closed-loop system. Compared with previous global asymptotic tracking ANC approaches, the proposed approach not only simplifies the selection of PD gain, but also relaxes chattering at the control input. Simulation results have demonstrated the effectiveness of the proposed approach.
引用
收藏
页码：1268 / 1273
页数：5
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