Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter

被引:7
|
作者
Chowdhury, Amor [1 ]
Sarjas, Andrej [2 ]
机构
[1] Margento R&D, Gosposvetska Cesta 84, Maribor 2000, Slovenia
[2] Univ Maribor, Fac Elect Engn & Comp Sci, Smetanova 17, SLO-2000 Maribor, Slovenia
关键词
accurate proximity measurement; sensor fusion algorithm; Unscented Kalman Filter; finite element modelling; LEVITATION SYSTEM; DESIGN; FORCE;
D O I
10.3390/s16091504
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.
引用
收藏
页数:23
相关论文
共 9 条
  • [1] Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm
    Boada, B. L.
    Boada, M. J. L.
    Diaz, V.
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 72-73 : 832 - 845
  • [2] A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter
    Yin, Yufang
    Wang, Qiyu
    Zhang, Huijie
    Xu, Hong
    WIRELESS PERSONAL COMMUNICATIONS, 2021, 117 (02) : 607 - 621
  • [3] A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter
    Yufang Yin
    Qiyu Wang
    Huijie Zhang
    Hong Xu
    Wireless Personal Communications, 2021, 117 : 607 - 621
  • [4] Position Control System Based on Inertia Measurement Unit Sensor Fusion with Kalman Filter
    Ishikawa, Takahiro
    Nozaki, Takahiro
    Murakami, Toshiyuki
    2016 IEEE 14TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC), 2016, : 153 - 159
  • [5] Sensor Data Fusion Using Unscented Kalman Filter for VOR-Based Vision Tracking System for Mobile Robots
    Anjum, Muhammad Latif
    Ahmad, Omar
    Bona, Basilio
    Cho, Dong-il Dan
    TOWARDS AUTONOMOUS ROBOTIC SYSTEMS, 2014, 8069 : 103 - 113
  • [6] A new multi-sensor hierarchical data fusion algorithm based on unscented Kalman filter for the attitude observation of the wave glider
    Liu, Fen
    Liu, Yubing
    Sun, Xiujun
    Sang, Hongqiang
    APPLIED OCEAN RESEARCH, 2021, 109
  • [7] Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System
    Yuan, Xuebing
    Yu, Shuai
    Zhang, Shengzhi
    Wang, Guoping
    Liu, Sheng
    SENSORS, 2015, 15 (05) : 10872 - 10890
  • [8] BeiDou Navigation Satellite System/Inertial Measurement Unit Integrated Train Positioning Method Based on Improved Unscented Kalman Filter Algorithm
    Cai X.
    Wang C.
    Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 2020, 55 (02): : 393 - 400
  • [9] Optimal design of multi-coil system for generating uniform magnetic field based on intelligent optimization algorithm and finite element method
    Lyu Z.
    Zhang J.
    Wang S.
    Zhao X.
    Li T.
    Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (05): : 980 - 988