Adaptive Neural Network-Based Finite-Time Impedance Control of Constrained Robotic Manipulators With Disturbance Observer

被引：44

作者：

Li, Gang ^{[1
]}

Chen, Xinkai ^{[2
]}

Yu, Jinpeng ^{[1
]}

Liu, Jiapeng ^{[1
]}

机构：

[1] Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China

[2] Shibaura Inst Technol, Dept Elect & Informat Syst, Saitama 3378570, Japan

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2022年 / 69卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Manipulator dynamics; Disturbance observers; Artificial neural networks; Mathematical model; Impedance; Adaptive systems; Trajectory; Adaptive neural network; disturbance observer; command filtered; finite-time control; full state constraints;

D O I：

10.1109/TCSII.2021.3109257

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This brief proposes an adaptive neural network-based finite-time impedance control method for constrained robotic manipulators with disturbance observer. Firstly, by combining barrier Lyapunov functions with the finite-time stability control theory, the control system has a faster convergence rate without violating the full state constraints. Secondly, the adaptive neural network is introduced to approximate the unmodeled dynamics and a disturbance observer is designed to compensate for the unknown time-varying disturbances. Then, the command filtered control technique with error compensation mechanism is used to deal with the "explosion of complexity" of traditional backstepping and improve the control accuracy. The simulation results show the effectiveness of the proposed control method.

引用

页码：1412 / 1416

页数：5

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