State and force observers based on multibody models and the indirect Kalman filter

被引:40
|
作者
Sanjurjo, Emilio [1 ]
Dopico, Daniel [1 ]
Luaces, Alberto [1 ]
Angel Naya, Miguel [1 ]
机构
[1] Univ A Coruna, Escuela Politecn Super, Mech Engn Lab, Mendizabal S-N, Ferrol 15403, Spain
关键词
Multibody dynamics; Kalman filter; State observer; Force estimation; SYSTEMS;
D O I
10.1016/j.ymssp.2017.12.041
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The aim of this work is to present two new methods to provide state observers by combining multibody simulations with indirect extended Kalman filters. One of the methods presented provides also input force estimation. The observers have been applied to two mechanism with four different sensor configurations, and compared to other multibodybased observers found in the literature to evaluate their behavior, namely, the unscented Kalman filter (UKF), and the indirect extended Kalman filter with simplified Jacobians (errorEKF). The new methods have some more computational cost than the errorEKF, but still much less than the UKF. Regarding their accuracy, both are better than the errorEKF. The method with input force estimation outperforms also the UKF, while the method without force estimation achieves results almost identical to those of the UKF. All the methods have been implemented as a reusable MATLAB (R) toolkit which has been released as Open Source in https://github.com/MBDS/mbde-matlab. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:210 / 228
页数:19
相关论文
共 50 条
  • [21] Applications of Disturbance Observer and Kalman Filter Based Force Sensation in Motion Control
    Thao Tran Phuong
    Ohishi, Kiyoshi
    Yokokura, Yuki
    Takei, Yoshinori
    2018 IEEE 15TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC), 2018, : 625 - 630
  • [22] AhRS development using still-state estimation and indirect Kalman filter
    Hong H.
    Won M.
    Transactions of the Korean Institute of Electrical Engineers, 2019, 68 (06): : 797 - 803
  • [23] Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method
    Yang, Rui
    Li, Li-Yi
    Wang, Ming-Yi
    Zhang, Cheng-Ming
    Zeng-Gu, Yi-Ming
    IEEE ACCESS, 2019, 7 : 108331 - 108342
  • [24] Force localization and reconstruction based on a novel sparse Kalman filter
    Feng, Wei
    Li, Qiaofeng
    Lu, Qiuhai
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 144
  • [25] Experimental Testing of Observers Comprising Discrete Kalman Filter and High-Gain Observers
    Ali, Danish
    Asim, M.
    Wallam, Fahad
    Qazi, Zia-ul-Haque
    Abbas, Adeel
    Naudhani, Yousuf
    2019 2ND INTERNATIONAL CONFERENCE ON COMPUTING, MATHEMATICS AND ENGINEERING TECHNOLOGIES (ICOMET), 2019,
  • [26] Stochastic propeller force and moment reconstruction at a shaft end based on an improved Kalman filter
    Sun, Yue
    Tong, Xiandong
    Li, Zengguang
    Chen, Yong
    MEASUREMENT, 2023, 206
  • [27] A Kalman filter based ARX time series modeling for force identification on flexible manipulators
    Nguyen, Quoc-Cuong
    Vu, Viet-Hung
    Thomas, Marc
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 169
  • [28] On Extended State Based Kalman-Bucy Filter
    Zhang, Xiaocheng
    Xue, Wenchao
    Fang, Haitao
    He, Xingkang
    PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS), 2018, : 1158 - 1163
  • [29] On the evaluation of uncertainties for state estimation with the Kalman filter
    Eichstaedt, S.
    Makarava, N.
    Elster, C.
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2016, 27 (12)
  • [30] Grasping force estimation using state-space model and Kalman Filter
    Dutra, Bruno
    Silveira, Antonio
    Pereira, Antonio
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 70