• DocumentCode
    2307677
  • Title

    Predictive multiple model switching control with the self-organizing map

  • Author

    Motter, Mark A.

  • Author_Institution
    NASA Langley Res. Center, Hampton, VA, USA
  • Volume
    4
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    317
  • Abstract
    A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framework. Multiple SOMs collectively model the global response of a nonautonomous system to a finite set of representative prototype controls. Each SOM provides a codebook representation of the dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the global minimization of a similarity metric. The SOM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme that selects the best available model for the applied control. SOM based linear models are used to predict the response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal
  • Keywords
    dynamics; minimisation; neurocontrollers; nonlinear control systems; predictive control; self-organising feature maps; codebook representation; dynamic regimes; global minimization; local dynamical regime; nonautonomous system; nonlinear autonomous systems; predictive multiple model switching control; self-organizing map local dynamic modeling; similarity metric; topological neighborhoods; Control system synthesis; Control systems; Lattices; NASA; Nonlinear control systems; Nonlinear dynamical systems; Postal services; Predictive models; Prototypes; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
  • Conference_Location
    Como
  • ISSN
    1098-7576
  • Print_ISBN
    0-7695-0619-4
  • Type

    conf

  • DOI
    10.1109/IJCNN.2000.860791
  • Filename
    860791