Experimental Validation of Functional Iterative Learning Control on a One-Link Flexible Arm

Conference Paper (2023)
Author(s)

S.C. Drost (Student TU Delft)

P. Pustina (TU Delft - Learning & Autonomous Control)

Franco Angelini (University of Pisa)

Alessandro De Luca (Sapienza University of Rome)

Gerwin Smit (TU Delft - Medical Instruments & Bio-Inspired Technology)

Cosimo Della Lieu (TU Delft - Learning & Autonomous Control)

Research Group
Learning & Autonomous Control
Copyright
© 2023 S.C. Drost, P. Pustina, Franco Angelini, Alessandro De Luca, G. Smit, C. Della Santina
DOI related publication
https://doi.org/10.1109/ICRA48891.2023.10161397
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 S.C. Drost, P. Pustina, Franco Angelini, Alessandro De Luca, G. Smit, C. Della Santina
Research Group
Learning & Autonomous Control
Pages (from-to)
5291-5297
ISBN (print)
979-8-3503-2365-8
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Performing precise, repetitive motions is essential in many robotic and automation systems. Iterative learning control (ILC) allows determining the necessary control command by using a very rough system model to speed up the process. Functional iterative learning control is a novel technique that promises to solve several limitations of classic ILC. It operates by merging the input space into a large functional space, resulting in an over-determined control task in the iteration domain. In this way, it can deal with systems having more outputs than inputs and accelerate the learning process without resorting to model discretizations. However, the framework lacks so far a validation in experiments. This paper aims to provide such experimental validation in the context of robotics. To this end, we designed and built a one-link flexible arm that is actuated by a stepper motor, which makes the development of an accurate model more challenging and the validation closer to the industrial practice. We provide multiple experimental results across several conditions, proving the feasibility of the method in practice.

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