A Concept for Optimizing Motor Control Parameters Using Bayesian Optimization

Henning Cui, Markus Görlich-Bucher, Lukas Rosenbauer, Jörg Hähner, Daniel Gerber

2023

Abstract

Electrical motors need specific parametrizations to run in highly specialized use cases. However, finding such parametrizations may need a lot of time and expert knowledge. Furthermore, the task gets more complex as multiple optimization goals interplay. Thus, we propose a novel approach using Bayesian Optimization to find optimal configuration parameters for an electric motor. In addition, a multi-objective problem is present as two different and competing objectives must be optimized. At first, the motor must reach a desired revolution per minute as fast as possible. Afterwards, it must be able to continue running without fluctuating currents. For this task, we utilize Bayesian Optimization to optimize parameters. In addition, the evolutionary algorithm NSGA-II is used for the multi-objective setting, as NSGA-II is able to find an optimal pareto front. Our approach is evaluated using three different motors mounted to a test bench. Depending on the motor, we are able to find good parameters in about 60-100%.

Paper Citation

in Harvard Style

Cui H., Görlich-Bucher M., Rosenbauer L., Hähner J. and Gerber D. (2023). A Concept for Optimizing Motor Control Parameters Using Bayesian Optimization. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 107-114. DOI: 10.5220/0012093700003543

in Bibtex Style

@conference{icinco23,
author={Henning Cui and Markus Görlich-Bucher and Lukas Rosenbauer and Jörg Hähner and Daniel Gerber},
title={A Concept for Optimizing Motor Control Parameters Using Bayesian Optimization},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={107-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012093700003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - A Concept for Optimizing Motor Control Parameters Using Bayesian Optimization
SN - 978-989-758-670-5
AU - Cui H.
AU - Görlich-Bucher M.
AU - Rosenbauer L.
AU - Hähner J.
AU - Gerber D.
PY - 2023
SP - 107
EP - 114
DO - 10.5220/0012093700003543
PB - SciTePress