Sensorless position detection of reluctance spherical motors based on inductive characteristics

Abstract

The rotor attitude detection of spherical motors usually requires the installation of position sensors on the rotor shaft or stator to achieve, which occupy a large space and cause negative effects for the practical installation and application of spherical motors. In this paper, a sensorless position detection method for reluctance spherical motors based on the structure and operating characteristics of reluctance spherical motors is proposed. By detecting the stator coil inductance data at different rotor attitude angles, the position prediction model is constructed using the extreme learning machine algorithm. Through comparison and validation, it is demonstrated that the rotor attitude angle obtained by the method is highly accurate, providing a feasible idea for the control and application of reluctance spherical motors.

Keywords

Sensorless detection sphercial motors inductance characteristics extreme learning machine

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References

and Zhou

, Torque modelling and validation for a spherical motor with stepped permanent magnets, Journal of Electrical Engineering & Technology 15 (2020), 2661.

Wen

and Wang

, Modeling and analysis of permanent magnet spherical motors by a multi-task gaussian process method and finite element method for output torque, IEEE Transactions on Industrial Electronics 99 (2020), 1.

Tao

and Wang

, Nonlinear modeling analysis of a reluctance spherical motor based on torque-angle characteristics, International Journal of Applied Electromagnetics and Mechanics 62 (2019), 1.

Zeng

Wang

and Zhang

, Mechanism and suspension control study for spherical reluctance motor with magnetic levitation, Advanced Materials Research 915 (2012), 400.

Oner

Cetin

and Yilanci

, Design and performance evaluation of a photovoltaic sun-tracking system driven by a three-freedom spherical motor, International Journal of Exergy 6 (2009), 853.

Park

D.H.

Ryu

S.R.

and Park

H.J.

, Application of similarity measure for fuzzy C-means clustering to power system management, International Journal of Fuzzy Logic & Intelligent Systems 8 (2008), 18.

Bai

Lee

K.M.

and Lu

, A magnetic flux model based method for detecting multi-DOF motion of a permanent magnet spherical motor, Mechatronics 39 (2016), 217.

Bai

, A sensor-less motion sensing method of a 3-DOF permanent magnet spherical motor, in: Ifac Proceedings Volumes, 2013.

Mashimo

Awaga

and Toyama

, Development of a spherical ultrasonic motor with an attitude sensing system using optical fibers IEEE International Conference on Robotics & Automation, IEEE (2007).

10.

Yan

Chen

I.M.

and Guo

, A three degree-of-freedom optical orientation measurement method for spherical actuator applications, IEEE Transactions on Automation Science & Engineering 8 (2011), 319.

11.

Feng

Huang

G.B.

and Lin

, Error minimized extreme learning machine with growth of hidden nodes and incremental learning, IEEE Transactions on Neural Networks 20 (2009), 1352.

12.

and Lin

, M-Weighted extreme learning machine for imbalanced learning IEEE International Conference on Information & Automation, IEEE 242 (2017).

13.

Wan

and Pinson

, Probabilistic forecasting of wind power generation using extreme learning machine, IEEE Transactions on Power Systems 29 (2014), 1033.

14.

Chen

and Su

, Local binary patterns and extreme learning machine for hyperspectral imagery classification, IEEE Transactions on Geoscience & Remote Sensing 53 (2015), 3681.

15.

Tang

Deng

and Huang

G.B.

, Extreme learning machine for multilayer perceptron, IEEE Transactions on Neural Networks & Learning Systems 27 (2017), 809.

16.

Gattineni

and Dharan

G.R.S.

, Intrusion detection mechanisms: SVM, random forest, and extreme learning machine, in: 2021 Third International Conference on Inventive Research in Computing Applications, 2021, pp. 273–276.

17.

Chen

Hai

and Wang

, Multiobjective parameters optimization of extreme learning machine based on MOEA/D, IEEE International Conference on Artificial Intelligence and Computer Applications (2021), 321–324.