A learning algorithm for hybrid force control of robot arms

Author

Lucibello, Pasquale

Author_Institution

Dipartmento di Inf. e Sistemistica, Roma Univ., Italy

fYear

1993

fDate

2-6 May 1993

Firstpage

654

Abstract

An investigation of the hybrid force control of robot arms by learning is presented. A force control scheme based on feedback linearization is used to build an algorithm that improves, trial by trial, force and position tracking over a finite time interval. Unlike other published learning 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 proved by means of the contraction mapping principle

Keywords

adaptive control; feedback; force control; learning (artificial intelligence); linearisation techniques; manipulators; position control; tracking; contraction mapping principle; convergence; feedback linearization; hybrid force control; learning algorithm; manipulators; position tracking; robot arms; robustness; Convergence; Error correction; Force control; Force feedback; H infinity control; Manipulators; Orbital robotics; Robots; Robust control; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on

Conference_Location

Atlanta, GA

Print_ISBN

0-8186-3450-2

Type

conf

DOI

10.1109/ROBOT.1993.292053

Filename

292053

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1865488