Indirect Adaptive learning of Acceleration feedback control for Chained Multiple Mass-Spring-Damper Systems

Author

Dieulot, J.Y. ; Colas, F. ; Barre, P.-J. ; Borne, P.

Author_Institution

ENSAM, Lille

fYear

2007

fDate

10-14 April 2007

Firstpage

2495

Lastpage

2500

Abstract

An indirect iterative learning algorithm has shown to be able to update the parameters of an acceleration feedback controller for flexible manipulators. The fine estimation of the masses of a chain of mass-spring-dampers units allows the simultaneous tuning of both the feedback controller and the trajectory generation. This algorithm has been validated on an industrial robot arm.

Keywords

acceleration control; adaptive control; feedback; flexible manipulators; iterative methods; learning systems; position control; acceleration feedback control; chained multiple mass-spring-damper systems; flexible arms; flexible manipulators; indirect adaptive learning; indirect iterative learning algorithm; industrial robot arm; path planning; trajectory generation; Acceleration; Adaptive control; Feedback control; Iterative algorithms; Manipulators; Programmable control; Service robots; Servomechanisms; Trajectory; Vibration control; Calibration and Identification; Flexible Arms; Iterative Learning Control; Path Planning for Manipulators;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2007 IEEE International Conference on

Conference_Location

Roma

ISSN

1050-4729

Print_ISBN

1-4244-0601-3

Electronic_ISBN

1050-4729

Type

conf

DOI

10.1109/ROBOT.2007.363840

Filename

4209458