• DocumentCode
    1682116
  • Title

    Approximating optimal state feedback using neural networks

  • Author

    Mcdermott, Wesley ; Athans, Michael

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA
  • Volume
    3
  • fYear
    1994
  • Firstpage
    2466
  • Abstract
    The training and usage of multilayer neural networks on discontinuous (e.g. bang-bang) feedback control problems are discussed. Training sets are created from optimal open loop trajectory information and a heuristic for trimming the base data set is presented. A priori knowledge about solution trajectories is seen to improve the training process
  • Keywords
    feedforward neural nets; neurocontrollers; optimal control; state feedback; base data set; discontinuous feedback control; multilayer neural networks; optimal open loop trajectory; optimal state feedback; Computer science; Cost function; Feedback control; Multi-layer neural network; Neural networks; Open loop systems; Optimal control; Reflection; State feedback; State-space methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
  • Conference_Location
    Lake Buena Vista, FL
  • Print_ISBN
    0-7803-1968-0
  • Type

    conf

  • DOI
    10.1109/CDC.1994.411510
  • Filename
    411510
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1682116