Train Velocity Estimation by Extended Kalman Filter

被引:0
|
作者
Pichlik, Petr [1 ]
Zdenek, Jiri [1 ]
机构
[1] Czech Tech Univ, Dept Elect Drives & Tract, Fac Elect Engn, Tech 2, Prague 16627, Czech Republic
关键词
extended Kalman filter; train velocity; slip velocity; system model; nonlinear estimation; slip control;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A train longitudinal velocity is needed for some control devices. The train velocity is typically determined from a locomotive wheelsets velocity. This solution can cause problems when the wheelsets are driven or braked. In these cases, the wheelset velocity is higher or lower than the train longitudinal velocity due to a slip or skid velocity. The problem is typically solved by some type of averaging or filtration of all available wheelsets velocities. These algorithms need to know all wheelsets velocities at the same time. This task can be difficult to fulfil in some types of distributed computer systems due to communication delays between computers. The problem is solved by using an extended Kalman filter that estimates the train velocity from one wheelset velocity in the paper. The filter and its properties are described and designed in the paper. The measured data are used for the filter function check.
引用
收藏
页数:4
相关论文
共 50 条
  • [31] A Comparison between State of Charge Estimation Methods: Extended Kalman Filter and Unscented Kalman Filter
    Ilies, Adelina Ioana
    Chindris, Gabriel
    Pitica, Dan
    2020 IEEE 26TH INTERNATIONAL SYMPOSIUM FOR DESIGN AND TECHNOLOGY IN ELECTRONIC PACKAGING (SIITME 2020), 2020, : 376 - 381
  • [32] Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter
    Kim, Dongmyoung
    Jeon, Taesu
    Paek, Insu
    Roynarin, Wirachai
    APPLIED SCIENCES-BASEL, 2024, 14 (19):
  • [33] Legged Robot State Estimation in Slippery Environments Using Invariant Extended Kalman Filter with Velocity Update
    Teng, Sangli
    Mueller, Mark Wilfried
    Sreenath, Koushil
    2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021), 2021, : 3104 - 3110
  • [34] Improving Inertial Velocity Estimation Through Magnetic Field Gradient-based Extended Kalman Filter
    Zmitri, Makia
    Fourati, Hassen
    Prieur, Christophe
    2019 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2019,
  • [35] An extended Kalman filter applied to a pneumatic servo system - velocity and acceleration estimation in a clutch actuation application
    Kaasa, GO
    Chapple, PJ
    Lie, B
    POWER TRANSMISSION AND MOTION CONTROL PTMC 2001, 2001, : 293 - 308
  • [36] Improvement of Extended Kalman Filter Using Invariant Extended Kalman Filter
    Ko, Nak Yong
    Song, Gyeongsub
    Youn, Wonkeun
    Choi, In Ho
    Kim, Tae Sik
    2018 18TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), 2018, : 948 - 950
  • [37] Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter
    Garcia, R. V.
    Pardal, P. C. P. M.
    Kuga, H. K.
    Zanardi, M. C.
    ADVANCES IN SPACE RESEARCH, 2019, 63 (02) : 1038 - 1050
  • [38] Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation附视频
    杨亚非
    Journal of Measurement Science and Instrumentation, 2011, (04) : 307 - 311
  • [39] Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter
    Liang, Tiantian
    Li, Kexin
    Wang, Yingdong
    Wang, Mao
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2023, 45 (14) : 2688 - 2711
  • [40] Dynamic Fundamental and Harmonic Synchrophasor Estimation by Extended Kalman Filter
    Ferrero, Roberto
    Pegoraro, Paolo Attilio
    Toscani, Sergio
    2016 IEEE INTERNATIONAL WORKSHOP ON APPLIED MEASUREMENTS FOR POWER SYSTEMS (AMPS), 2016, : 137 - 142