• DocumentCode
    2212383
  • Title

    Fault detection for an active vehicle suspension

  • Author

    Fischer, Daniel ; Kaus, Eberhard ; Isermann, Rolf

  • Author_Institution
    Inst. of Autom. Control, Darmstadt Univ. of Technol., Germany
  • Volume
    5
  • fYear
    2003
  • fDate
    4-6 June 2003
  • Firstpage
    4377
  • Abstract
    After a short introduction into the topic of active vehicle suspension systems, a mathematical model of the used active vehicle suspension, which is presented in a test rig, is derived. It is shown how the unknown parameters can be obtained experimentally by parameter estimation. Using parameter estimation and LOLIMOT - a special type of neuronal networks, models of the active suspension are identified. These models are used for model based fault detection and identification, in order to obtain reliable knowledge of the system´s state. All results are shown for measurements from an active suspension on a test rig.
  • Keywords
    fault diagnosis; neural nets; parameter estimation; road vehicles; LOLIMOT; active vehicle suspension system; fault detection; identification; local linear model tree; model based fault detection; neuronal networks; parameter estimation; parity equations; systems state; Control systems; Equations; Fault detection; Fault diagnosis; Frequency; Mechatronics; Parameter estimation; System testing; Vehicle dynamics; Vehicle safety;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 2003. Proceedings of the 2003
  • ISSN
    0743-1619
  • Print_ISBN
    0-7803-7896-2
  • Type

    conf

  • DOI
    10.1109/ACC.2003.1240527
  • Filename
    1240527