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research article

Iterative learning control for trajectory tracking of a parallel Delta robot

Boudjedir, Chems Eddine
•
Bouri, Mohamed  
•
Boukhetala, Djamel
February 1, 2019
At-Automatisierungstechnik

This paper proposes an iterative learning controller (ILC) under the alignment condition for trajectory tracking of a parallel Delta robot, that performs various repetitive tasks for palletization. Motivated by the high cadence of our application that leads to significant coupling effects, where the traditional PD/PID fail to satisfy the requirements performances. A PD-type ILC is combined with a PD controller in order to enhance the performance through iterations during the whole operation interval. The traditional resetting condition is replaced by the practical alignment condition, then the convergence of the tracking error is derived based on the Lyapunov's theory. We definitely point out that the position and velocity errors decrease as the number of iterations increases. Experiments are carried out to demonstrate the effectiveness of the proposed controller.

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Type
research article
DOI
10.1515/auto-2018-0086
Web of Science ID

WOS:000457192300005

Author(s)
Boudjedir, Chems Eddine
Bouri, Mohamed  
Boukhetala, Djamel
Date Issued

2019-02-01

Publisher

WALTER DE GRUYTER GMBH

Published in
At-Automatisierungstechnik
Volume

67

Issue

2

Start page

145

End page

156

Subjects

Automation & Control Systems

•

iterative learning control

•

alignment condition

•

delta robot

•

lyapunov theory

•

pd control

•

trajectory tracking

•

nonparametric uncertainties

•

systems

•

design

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
BIOROB  
Available on Infoscience
June 18, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/157097
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