This paper proposes an adaptive strong tracking Kalman filters (ASTKF) for reducing the measurement deviation of speed and improving the tracking ability of the observer. The proposed ASTKF can limit the tracking mismatch caused by low- resolution encoders and track the load torque better in real time. The proposed ASTKF introduces a suboptimal scaling factor to the gain matrix and calculating the system noise matrix at the current time. The simulations illustrate the ASTKF can precisely obtain the information of both speed and load torque. And it can achieve smaller measurement deviation and faster response.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Speed measurement error reduction via adaptive strong tracking Kalman filters


    Contributors:
    Liu, Zirui (author) / Yang, Ming (author) / Long, Jiang (author) / Xu, Dianguo (author)


    Publication date :

    2017-08-01


    Size :

    513846 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Worst error performance of continuous Kalman filters

    Nishimura, T. | Tema Archive | 1975



    Spacecraft tracking using sampled-data Kalman filters

    Teixeira, B.O.S. / Santillo, M.A. / Erwin, R.S. et al. | Tema Archive | 2008