On Improved Commutation for Moving-Magnet Planar Actuators

Yorick Broens (Corresponding author), Hans Butler, Roland Tóth

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Abstract

The demand for high-precision and high-throughput motion control systems has increased significantly in recent years. The use of moving-magnet planar actuators (MMPAs) is gaining popularity due to their advantageous characteristics, such as complete environmental decoupling and reduction of stage mass. Nonetheless, model-based commutation techniques for MMPAs are compromised by misalignment between the mover and coil array and mismatch between the ideal electromagnetic model and the physical system, often leading to decreased system performance. To address this issue, a novel improved commutation approach is proposed in this letter, which is applicable for general planar motor applications, by means of dynamic regulation of the position dependence of the ideal model-based commutation algorithm, which allows for attenuation of magnetic misalignment, manufacturing inaccuracies and other unmodelled phenomena. The effectiveness of the proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype.

Original languageEnglish
Article number10159021
Pages (from-to)2593-2598
Number of pages6
JournalIEEE Control Systems Letters
Volume7
DOIs
Publication statusPublished - 2023

Funding

This work was supported in part by the ECSEL Joint Undertaking (JU) Programme under Grant 875999, and in part by the European Union within the framework of the National Laboratory for Autonomous Systems under Grant RRF-2.3.1-21.2022- 00002.

FundersFunder number
National Laboratory for Autonomous SystemsRRF-2.3.1-21.2022- 00002
European Commission
Electronic Components and Systems for European Leadership875999

    Keywords

    • control applications
    • machine learning
    • Mechatronics

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