KMFDSST Algorithm-Based Rotor Attitude Estimation for a Spherical Motor

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 3; pp. 1 - 10
• Main Authors: Zhou, Sili, Cao, Wenping, Wang, Qunjing, Zhou, Mengran, Zheng, Xiaoliang, Lou, Jiachuan, Chen, Yong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Attitudes; Covariance matrices; Estimation; Kalman filter-based multi-object fast discriminative scale space tracker (KMFDSST); Kalman filters; Mechanical systems; Microelectromechanical systems; Motor rotors; Occlusion; permanent magnet spherical motor (PMSpM); rotor attitude estimation (RAE); Rotors; spherical motor; Stators; Target tracking; Visual discrimination; visual method; Visualization
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2023.3323709

Rotor attitude estimation (RAE) is a predominant approach to controlling spherical motors, but there is a margin for its improvement. This article proposes a visual RAE method by using the Kalman filter-based multi-object fast discriminative scale space tracker (KMFDSST) algorithm. The KMFDSST algorithm is adopted to detect three visual objects simultaneously on the top of the spherical motor. The rotor attitude is estimated based on the positions of the three objects. To verify the accuracy and dynamic performance of the KMFDSST algorithm when the occlusion cases happened at large tilt angles, the one-object tracking simulations are conducted among the KMFDSST, fast discriminative scale space tracker (FDSST), and multi-object Kalman kernelized correlation filter (MKKCF) algorithms. Simulation and experiment results indicate that the robustness of the KMFDSST algorithm is better than that of both MKKCF and FDSST algorithms. Moreover, the comparative experiment between the KMFDSST and micro-electro mechanical system (MEMS) RAE methods shows the advantages of the proposed RAE method.