Authors:
Henning Cui
1
;
Markus Görlich-Bucher
1
;
Lukas Rosenbauer
2
;
Jörg Hähner
1
and
Daniel Gerber
2
Affiliations:
1
Organic Computing Group, University of Augsburg, Am Technologiezentrum 8, 86159 Augsburg, Germany
;
2
BSH Hausgeräte GmbH, Im Gewerbepark B35, 93059 Regensburg, Germany
Keyword(s):
Bayesian Optimization, DC-Motor, Motor Control, Multiple-Objective, NSGA-II.
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 pa
rameters in about 60-100%.
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