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

Gang Li, Xinkai Chen, Jinpeng Yu, Jiapeng Liu

研究成果: Article査読

3 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1412-1416
ページ数5
ジャーナルIEEE Transactions on Circuits and Systems II: Express Briefs
69
3
DOI
出版ステータスPublished - 2022 3月 1

ASJC Scopus subject areas

  • 電子工学および電気工学

フィンガープリント

「Adaptive Neural Network-Based Finite-Time Impedance Control of Constrained Robotic Manipulators with Disturbance Observer」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル