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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. [ABSTRACT FROM AUTHOR]

This article proposes a rotor attitude estimation (RAE) method for spherical motors using the multiobject Kalman kernel correlation filter (MKKCF) algorithm in monocular vision. A permanent magnet spherical motor (PMSpM) is adopted as the research object, and a monocular area scan camera is equipped in the RAE system with a visual feature component mounted on top of the rotor output shaft. In the proposed MKKCF algorithm, the Kalman filter is used to enhance the robustness and accuracy of KCF tracker for each object. To simplify the proposed algorithm verification, a one-object tracking comparison is conducted among the KCF algorithm, the fast discriminative scale space tracking (FDSST) algorithm, and the Kalman KCF algorithm, and the results show that Kalman KCF tracker is more applicable for RAE. In addition, an RAE test bench is developed, and the MKKCF-based RAE method and the micro-electro-mechanical system (MEMS) (MPU9250) method are compared when estimating the rotor attitude. To set a benchmark, the contact rotor position measurement with encoders is used. The comparison results indicate that the MKKCF-based RAE method works with higher accuracy than the MEMS method. Finally, a closed-loop PMSpM control experiment is conducted by using the proposed MKKCF-based RAE method, and the practicability of the proposed method is proved. [ABSTRACT FROM AUTHOR]

In this paper, a sensorless rotor posture detection method based on the mutual inductance voltage of the stator coil is proposed to simplify the position detection element of a reluctance spherical motor. Firstly, the numerical relationship between the stator/rotor pole misalignment angle and the mutual inductance voltage of the stator coil is analyzed, which is used as the basis for judging the spatial position of the rotor. Secondly, an experimental platform is designed to verify the consistency between the calculated value and the experimental value of the mutual inductance voltage and to determine the appropriate excitation signal. Thirdly, based on the real-time voltages generated by the stator coil mutual inductance, an intelligent algorithm is used to invert the 3-DoF (degree-of-freedom) position angle of the spherical rotor combined with the motor structure constraints. The experimental results show that the detection method has a good on-line detection effect, and the population standard deviation is within 1.8° Therefore, the developed technique can be used for replacing the position detection method with sensors. [ABSTRACT FROM AUTHOR]