BLDC Motors Sensorless Control Based on MLP Topology Neural Network

Guozhong Yao, Jiayu Feng, Guiyong Wang () and Shaojun Han
Additional contact information
Guozhong Yao: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Jiayu Feng: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Guiyong Wang: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Shaojun Han: Wuxi Weifu Group High Tech Co., Ltd., Wuxi 214000, China

Energies, 2023, vol. 16, issue 10, 1-18

Abstract: In order to reduce the complexity of the brushless DC motor (BLDC)-control-system algorithm while improving the estimation performance of the rotor phase position and the speed of the sensorless motor, a neural network (ANN) control algorithm based on multi-layer perceptron (MLP) topology is proposed. The phase voltage of the motor is conditioned to obtain the phase-voltage signal with a high signal-to-noise ratio, which is used as the input eigenvalue of the multi-layer-perceptron-topology neural network algorithm. The encoder signal is used as the training test data of the MLP-ANN. The first layer of the perceptual neural network estimates the position according to the voltage characteristics with incremental time characteristics. The second layer of the perceptual neural network estimates the speed according to the collected time characteristics and the characteristics of rotor position error. The algorithm after learning and training is digitally discretized and integrated into the motor control system. Experimental tests were carried out under no-load, speed step and load mutation conditions. The experimental results show that the algorithm can accurately estimate the rotor position and speed. The absolute error of the rotor position is within 0.02 rad, and the absolute error of the rotor speed is within 4 rpm. The control system with strong robustness has good dynamic and static characteristics.

Keywords: brushless DC motor; neural network; sensorless control; multi-layer perceptron; phase voltage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/10/4027/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/10/4027/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:10:p:4027-:d:1144402

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().