期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2023.3323709
关键词
Rotors; Visualization; Stators; Kalman filters; Estimation; Covariance matrices; Target tracking; Kalman filter-based multi-object fast discriminative scale space tracker (KMFDSST); permanent magnet spherical motor (PMSpM); rotor attitude estimation (RAE); spherical motor; visual method
This article proposes a visual rotor attitude estimation method based on KMFDSST algorithm, which estimates the rotor attitude by simultaneously detecting the positions of three visual objects. Experimental results show that this method has better robustness.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据