Disturbance Observer and Kalman Filter Based Motion Control Realization

被引:39
|
作者
Thao Tran Phuong [1 ]
Ohishi, Kiyoshi [1 ]
Mitsantisuk, Chowarit [2 ]
Yokokura, Yuki [1 ]
Ohnishi, Kouhei [3 ]
Oboe, Roberto [4 ]
Sabanovic, Asif [5 ]
机构
[1] Nagaoka Univ Technol, 1603-1 Kamitomioka, Nagaoka, Niigata 9402188, Japan
[2] Kasetsart Univ, Bangkok, Thailand
[3] Keio Univ, Kohoku Ku, 3-14-1 Hiyoshi, Yokohamak, Kanagawa 2238522, Japan
[4] Univ Padua, Dept Management & Engn, Automat Control, Padua, Italy
[5] Int Univ Sarajevo, Sarajevo, Bosnia & Herceg
关键词
disturbance observer; Kalman filter; motion control; acceleration control; force control; real-world haptics;
D O I
10.1541/ieejjia.7.1
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Many effective robot-manipulator control schemes using a disturbance observer have been reported in the literature in the past decades. Besides, the disturbance observer combined with the Kalman filter has attracted the attention of researchers in the field of motion control. The major advantage of a motion control system based on the Kalman filter and disturbance observer is the realization of high robustness against disturbance and parameter variations, effective noise suppression and wideband force sensing. This paper presents a survey of motion control based on the Kalman filter and disturbance observer, which have been previously introduced by the authors. Several control schemes, as well as formulations and applications of the Kalman filter and disturbance observer, are described in the paper. The performance and effectiveness of the control schemes are evaluated to give a useful and comprehensive design of the Kalman filter and disturbance observer in various motion control applications.
引用
收藏
页码:1 / 14
页数:14
相关论文
共 50 条
  • [31] Disturbance estimator as a state observer with extended Kalman filter for robotic manipulator
    Vijyant Agarwal
    Harish Parthasarathy
    Nonlinear Dynamics, 2016, 85 : 2809 - 2825
  • [32] High performance force sensing based on kalman-filter-based disturbance observer utilizing FPGA
    Phuong T.T.
    Mitsantisuk C.
    Ohishi K.
    IEEJ Transactions on Industry Applications, 2011, 131 (03) : 334 - 342+15
  • [33] A Kalman Filter-based disturbance observer for state-of-charge estimation in EV batteries
    Rigatos, Gerasimos
    Busawon, Krishna
    Siano, Pierluigi
    Abbaszadeh, Masoud
    2018 AEIT INTERNATIONAL ANNUAL CONFERENCE, 2018,
  • [34] Tilting Control Based Motion Control on Inverted Pendulum Robots with Disturbance Observer
    Yongyai, Chaisamorn
    Shaimada, Akira
    Sonoda, Kenichi
    2009 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS, VOLS 1-3, 2009, : 1538 - +
  • [35] Design and realization of the kalman filter based on LabVIEW
    Tian, Jing
    Wang, Shaoqian
    COMPUTER AND INFORMATION TECHNOLOGY, 2014, 519-520 : 1276 - 1280
  • [36] Fully Active Suspension Design using Super Twisting Sliding Mode Control based on Disturbance Observer and Ensemble Kalman Filter
    Meetei, Leimapokpam Vidyaratan
    Das, Dushmanta Kumar
    2020 IEEE CALCUTTA CONFERENCE (CALCON), 2020, : 253 - 257
  • [37] Wideband motion control by acceleration disturbance observer
    Irie, Kouhei
    Katsura, Seiichiro
    Ohishi, Kiyoshi
    2006 12TH INTERNATIONAL POWER ELECTRONICS AND MOTION CONTROL CONFERENCE, VOLS 1-4, 2006, : 1754 - +
  • [38] Stabilization Control of Inverted Two-Wheeled Luggage Transport Vehicle Using a Kalman Filter-Based Disturbance Observer
    Matsubara, Hironori
    Nagatsu, Yuki
    Hashimoto, Hideki
    JOURNAL OF ROBOTICS AND MECHATRONICS, 2021, 33 (03) : 643 - 652
  • [39] Advanced motion control by multi-sensor based disturbance observer
    Irie, Kouhei
    Katsura, Seiichiro
    Ohishi, Kiyoshi
    9TH IEEE INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL, VOLS 1 AND 2, PROCEEDINGS, 2006, : 200 - +
  • [40] Disturbance Observer based Sliding Mode Control for Lateral Motion of an AUV
    Desai, Ravishankar P.
    Manjarekar, Narayan S.
    2021 SEVENTH INDIAN CONTROL CONFERENCE (ICC), 2021, : 165 - 170