Affiliations: [a] School of Electrical Engineering and Automation, Anhui University, Hefei, China | [b] Anhui Key Laboratory of Industrial Energy-Saving and Safety, Anhui University, Hefei, China | [c] Anhui Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control, Anhui University, Hefei, China | [d] National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei, China
Correspondence:
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Corresponding author: Q. Wang, School of Electrical Engineering and Automation, Anhui University, No.111 Jiulong Road, 230601, Hefei, China. E-mail: [email protected]
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.
