Adaptive neural network based prescribed performance control for teleoperation system under input saturation

被引：91

作者：

Yang, Yana ^{[1
]}

Ge, Chao ^{[2
]}

Wang, Hong ^{[2
]}

Li, Xiaoyi ^{[2
]}

Hua, Changchun ^{[1
]}

机构：

[1] Yanshan Univ, Inst Elect Engn, Qinhuangdao 066004, Peoples R China

[2] Hebei United Univ, Inst Informat Engn, Tangshan 063009, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2015年 / 352卷 / 05期

基金：

中国国家自然科学基金; 国家教育部博士点专项基金资助;

关键词：

BILATERAL TELEOPERATION; TRACKING CONTROL; NONLINEAR-SYSTEMS; ROBOT MANIPULATORS; FEEDBACK; SYNCHRONIZATION; DELAYS;

D O I：

10.1016/j.jfranklin.2015.01.032

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper addresses the stability and position synchronization problems for bilateral nonlinear teleoperation system with asymmetric constant time delays under input saturation. Compared with previous work, not only the steady-state performance but also the transient-state performance is considered. In the presence of system uncertainties and external disturbances, the corresponding adaptive neural network (ANN) based prescribed performance control (PPC) scheme is designed. Moreover, the time-dependent stability conditions are derived by applying the linear matrix inequality (LMI). Finally, in simulation comparisons with P+d (proportion plus differential) controller are conducted, simulation results are presented to demonstrate the effectiveness of the proposed PPC approach. (C) 2015 The Franldin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1850 / 1866

页数：17

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