• DocumentCode
    696368
  • Title

    Robustness assessment of model-based control for the Archimedes Wave Swing

  • Author

    Valerio, Duarte ; Beirao, Pedro ; Mendes, Mario J. G. C. ; Sa da Costa, Jose

  • Author_Institution
    IDMEC, TULisbon, Lisbon, Portugal
  • fYear
    2009
  • fDate
    23-26 Aug. 2009
  • Firstpage
    3749
  • Lastpage
    3754
  • Abstract
    In this paper the robustness of three model-based control strategies-internal model control (IMC) with linear models, IMC with neural network models, and feedback linearisation control-for the Archimedes Wave Swing (AWS), a device designed to produce electricity from the energy of sea waves, is assessed by checking how their performance, optimised for a neutral tide with a standard atmospheric pressure, changes under high and low tides, and under atmospheric pressure variations. The original AWS controller and latching control are used as a term of comparison. Simulation results show that, as a rule, low tides and lower atmospheric pressures lead to higher power productions, while high tides and higher atmospheric pressures lead to lower power productions; but, in spite of model maladjustments, model-based control strategies are not at disadvantage when compared with latching control.
  • Keywords
    atmospheric pressure; control system synthesis; feedback; linear systems; linearisation techniques; neurocontrollers; robust control; tides; wave power generation; wave power plants; AWS controller; AWS device design; Archimedes Wave Swing; IMC; electricity production; feedback linearisation control; high tides; internal model control; latching control; linear models; low tides; model-based control strategy; neural network models; neutral tide; performance checking; power production; robustness assessment; sea wave energy; standard atmospheric pressure; Artificial neural networks; Atmospheric modeling; Force; Mathematical model; Production; Robustness; Tides;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (ECC), 2009 European
  • Conference_Location
    Budapest
  • Print_ISBN
    978-3-9524173-9-3
  • Type

    conf

  • Filename
    7074983