文章基本信息

标题：Hybrid position–force control for constrained reconfigurable manipulators based on adaptive neural network
本地全文：下载
作者：Yuanchun Li ; Guogang Wang ; Bo Dong 等
期刊名称：Advances in Mechanical Engineering
印刷版ISSN：1687-8140
电子版ISSN：1687-8140
出版年度：2015
卷号：7
期号：9
DOI：10.1177/1687814015602409
语种：English
出版社：Sage Publications Ltd.
摘要：This article presents a hybrid position–force control method based on adaptive neural network for addressing the problems of position and force tracking of a constrained reconfigurable manipulator. The reduced-order dynamic model of a reconfigurable manipulator is formulated considering the model uncertainty and the external constraint. Combining decentralized control with centralized control scheme, a hybrid position–force controller is designed for controlling the position and force of the constrained reconfigurable manipulator. The dynamic model uncertainty and the dynamic coupling effect are compensated by radial basis function neural network. The stability of the closed-loop system is proved using the Lyapunov theory. Finally, simulations are performed to study the effectiveness of the proposed method.
关键词：Constrained reconfigurable manipulator; hybrid position–force control; reduced-order dynamic model; radial basis function neural network