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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]

A permanent magnet spherical motor (PMSpM) is a representative multiple degree-of-freedom electric apparatus. This paper proposes new analytical models for the PMSpM with stepped cylindrical permanent magnets (PMs). The research is not only to obtain the magnetic field and torque models of the PMSpM but also to yield a potential analytic tool for the spatial magnetic field and torque decomposition. The model decomposition aims to acquire a potential tool for the future research on PMSpM topology optimization and working mechanism. To achieve the two research targets above, the toroidal expansion method is used for magnetic field modeling, and the finite element method verification and experimental verification are conducted. Then, forces and torques are deduced from the Lorentz force law. In addition, a prototype PMSpM has been produced and the comparison studies of the air-gap flux density and torque among decomposed components are conducted using finite element analysis results and experimental results. It is concluded that the proposed analytical PMSpM models and the model decomposition method are proved. [ABSTRACT FROM AUTHOR]