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Efficient tuning for motion control in diverse systems: a Bayesian framework: a Bayesian framework

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Bibliographic Details
Title: Efficient tuning for motion control in diverse systems: a Bayesian framework: a Bayesian framework
Authors: Catenaro, E., Aarnoudse, L., Formentin, S., Oomen, T.
Source: IFAC-PapersOnLine. 58:354-359
Publisher Information: Elsevier BV, 2024.
Publication Year: 2024
Subject Terms: Model-free Optimization, Bayesian Optimization, Motion System, Iterative Learning Control, Feed-forward Control
Description: Feed-forward control is widely used in motion control systems that involve repetitive tasks, leading to substantial performance improvements. This paper presents a model-free feedforward optimization framework centred around Bayesian Optimization (BO). Bypassing the need for exhaustive system modelling, the method directly optimizes the Iterative Learning Control (ILC) degrees of freedom based on a user-defined parametrization of the feed-forward controller. Experimental results on a motion control application show significant improvements with respect to more classical ILC. A notable advantage emerges when dealing with an industrially relevant case with multiple similar plants; the optimizer is shown to adeptly adjust the feed-forward control to be compliant with the response of the measured system.
Document Type: Article
Language: English
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2024.08.554
Access URL: https://research.tue.nl/en/publications/8b01ef2e-36cb-442b-b158-98bc8cbc7568
https://doi.org/10.1016/j.ifacol.2024.08.554
Rights: Elsevier TDM
CC BY NC ND
Accession Number: edsair.doi.dedup.....477b07bfc8e0e6760ff005857ab10e99
Database: OpenAIRE
Description
Abstract:Feed-forward control is widely used in motion control systems that involve repetitive tasks, leading to substantial performance improvements. This paper presents a model-free feedforward optimization framework centred around Bayesian Optimization (BO). Bypassing the need for exhaustive system modelling, the method directly optimizes the Iterative Learning Control (ILC) degrees of freedom based on a user-defined parametrization of the feed-forward controller. Experimental results on a motion control application show significant improvements with respect to more classical ILC. A notable advantage emerges when dealing with an industrially relevant case with multiple similar plants; the optimizer is shown to adeptly adjust the feed-forward control to be compliant with the response of the measured system.
ISSN:24058963
DOI:10.1016/j.ifacol.2024.08.554