A learning algorithm for improved hybrid force control of robot arms

被引：3
|
作者：
Lucibello, P [1 ]
机构：
[1] Univ Roma La Sapienza, Dipartimento Informat & Sistemist, I-00184 Rome, Italy
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 1998年 / 28卷 / 02期
关键词：
D O I：
10.1109/3468.661151
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
An investigation on the hybrid force control of robot arms by learning is presented. A well-known force control scheme based on feedback linearization is used to build up an algorithm which improves, trial by trial, force and position tracking over a finite time interval. Differently from other published teaming control schemes, the proposed algorithm does not rely on high gain feedback. Robustness and convergence in spite of sufficiently small system parameter uncertainties and disturbances is proven by means of the contraction mapping principle.
引用
收藏
页码：241 / 244
页数：4
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