The Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applications

• Published in: Arabian journal for science and engineering (2011) Vol. 47; no. 3; pp. 2639 - 2648
• Main Authors: Unlersen, Muhammed Fahri, Balci, Selami, Aslan, Muhammet Fatih, Sabanci, Kadir
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022; Springer Nature B.V
• Subjects: Artificial intelligence; Brushless motors; Computer architecture; Computer simulation; D C motors; Dynamic response; Engineering; Finite element method; Humanities and Social Sciences; multidisciplinary; Parameters; Permanent magnets; Pulse duration; Recurrent neural networks; Research Article-Electrical Engineering; Rotor speed; Science; Long short-term memory; Rotor speed; Parameter estimation; Recurrent neural network; BLDC motor
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-021-05700-w

In this study, in order to determine the dynamic response of a four-pole permanent magnet three-phase brushless DC (BLDC) motor, parametric simulation studies are carried out with finite element analysis Rmxprt software depending on three specific input variables (excitation voltage, pulse width, and motor power). The rotor speed is defined as the output parameter to determine the dynamic response, and 600 parametric data are obtained according to the simulation studies. In order to estimate the rotor speed of the BLDC motor modeled using artificial intelligence (AI), an advanced recurrent neural network architecture known as bidirectional long short-term memory has been designed. Rotor speed is successfully estimated with the proposed architecture, and as a result, the mean absolute percentage error value is calculated as 3.25%. These results show that the analysis of BLDC motor parameters can be determined quickly with the proposed AI method without long-running simulations.