• DocumentCode
    3056036
  • Title

    An adaptive extended Kalman filter using artificial neural networks

  • Author

    Stubberud, Stephen C. ; Lobbia, Robert N. ; Owen, Mark

  • Author_Institution
    Orincon Corp., San Diego, CA, USA
  • Volume
    2
  • fYear
    1995
  • fDate
    13-15 Dec 1995
  • Firstpage
    1852
  • Abstract
    Develops an adaptive state-estimation technique using artificial neural networks, referred to as a neuro-observer. The neuro-observer is an extended Kalman filter structure that has its state-coupling function augmented by an artificial neural network that captures the unmodeled dynamics. The neural network of the neuro-observer trains on-line using an extended Kalman filter training paradigm. Improvement in the system model then provides for a more accurate state estimate in the feedback loop, thus enhancing the control signal so that the system behaves in a closer to optimal fashion
  • Keywords
    adaptive Kalman filters; feedback; neural nets; observers; adaptive extended Kalman filter; adaptive state-estimation technique; artificial neural networks; control signal; feedback loop; neuro-observer; state-coupling function; Additive noise; Artificial neural networks; Feedback control; Feedback loop; Jacobian matrices; Multi-layer neural network; Neural networks; Noise measurement; Optimal control; State estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
  • Conference_Location
    New Orleans, LA
  • ISSN
    0191-2216
  • Print_ISBN
    0-7803-2685-7
  • Type

    conf

  • DOI
    10.1109/CDC.1995.480611
  • Filename
    480611