A learning algorithm for improved hybrid force control of robot arms

Author

Lucibello, Pasquale

Author_Institution

Dipt. di Inf. e Sistemistica, Rome Univ., Italy

Volume

28

Issue

2

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

241

Lastpage

244

Abstract

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 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 proven by means of the contraction mapping principle

Keywords

feedback; force control; learning (artificial intelligence); linearisation techniques; manipulator dynamics; robust control; tracking; uncertain systems; contraction mapping principle; convergence; disturbances; feedback linearization; force tracking; improved hybrid force control; learning control schemes; parameter uncertainties; position tracking; robot arms; robustness; Convergence; Error correction; Force control; Force feedback; H infinity control; Manipulators; Orbital robotics; Robots; Robustness; Trajectory;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on

Publisher

ieee

ISSN

1083-4427

Type

jour

DOI

10.1109/3468.661151

Filename

661151