• DocumentCode
    921681
  • Title

    Solving large-scale control problems

  • Author

    Benner, Peter

  • Author_Institution
    Technische Univ. Chemnitz-Zwickau, Chemnitz, Germany
  • Volume
    24
  • Issue
    1
  • fYear
    2004
  • Firstpage
    44
  • Lastpage
    59
  • Abstract
    In this article we discuss sparse matrix algorithms and parallel algorithms, as well as their application to large-scale systems. For illustration, we solve the linear-quadratic regulator (LQR) problem and apply balanced truncation model reduction using either parallel computing or sparse matrix algorithms. We conclude that modern tools from numerical linear algebra, along with careful investigation and exploitation of the problem structure, can be used to derive algorithms capable of solving large control problems. Since these approaches are implemented in production-quality software, control engineers can employ complex models and use computational tools to analyse and design feedback control laws.
  • Keywords
    control system CAD; control system analysis computing; large-scale systems; linear quadratic control; parallel algorithms; reduced order systems; sparse matrices; balanced truncation model reduction; feedback control; large control problems; large-scale systems; linear-quadratic regulator; numerical linear algebra; parallel algorithms; parallel computing; production-quality software; sparse matrix algorithms; Computational modeling; Design engineering; Large-scale systems; Linear algebra; Parallel algorithms; Parallel processing; Reduced order systems; Regulators; Software tools; Sparse matrices;
  • fLanguage
    English
  • Journal_Title
    Control Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1066-033X
  • Type

    jour

  • DOI
    10.1109/MCS.2004.1272745
  • Filename
    1272745