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
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;
Conference_Titel :
Control Conference (ECC), 2009 European
Conference_Location :
Budapest
Print_ISBN :
978-3-9524173-9-3