Innovative Approach for the Determination of a DC Motor’s and Drive’s Parameters Using Evolutionary Methods and Different Measured Current and Angular Speed Responses

Marko Jesenik (), Miha Ravber and Mislav Trbušić
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Marko Jesenik: Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia
Miha Ravber: Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia
Mislav Trbušić: Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia

Mathematics, 2023, vol. 12, issue 1, 1-26

Abstract: The determination is presented of seven parameters of a DC motor’s drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters’ determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive’s acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the L a calculation. The deviations of all the other parameters’ calculations were less than 2%.

Keywords: Differential Evolution; Artificial Bee Colony; teaching-learning based optimization; DC motor; electric drive (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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