[Zurück]

M. Bibl, M. Robin, M. Steinegger, G. Schitter:
"Framework for Implementation of Iterative Learning Control on Programmable Logic Controllers";
Poster: 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016), Berlin; 06.09.2016 - 09.09.2016; in: "Proceedings of 2016 IEEE 21th Conference on Emerging Technologies & Factory Automation", (2016), ISBN: 978-1-5090-1314-2; Paper-Nr. 271, 4 S.

In this paper, an implementation approach for norm-
optimal iterative learning control (ILC) on programmable logic
controllers (PLCs) is presented. After a detailed conceptual
overview and discussion of the norm-optimal ILC algorithm,
the challenges for implementing ILC algorithms on PLCs are
discussed and an efficient three-phase implementation approach
is proposed. Here, the three phases consist of an offline calculation,
the calculation of the feedforward part between consecutive
iterations, and the online calculation of the current control
input. It is also shown that this separation enables the efficient
implementation of the norm-optimal ILC algorithm on standard
industrial controllers like PLCs. The proposed norm-optimal ILC
implementation approach is verified by a simulation of a gantry
robot with three degrees of freedom, where the norm-optimal
ILC algorithm is executed within a Soft-PLC.

ILC, PLC,Norm-optimal, robot

http://publik.tuwien.ac.at/files/PubDat_251133.pdf