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- Control of the archimedes wave swing using neural networksPublication . Beirão, Pedro; Mendes, Mário J. G. C.; Valério, Duarte; Costa, José Sá daThis paper addresses the control of the Archimedes Wave Swing, a fully-submerged Wave Energy Converter (WEC), of which a prototype has already been built and tested. Simulation results are presented in which Internal Model Control (IMC) is used, both with linear models and with non-linear neural network (NN) models. To the best of our knowledge this is the first time NN-based control is being applied to design a controller for a WEC. NNs are a mathematical tool suitable to model the behaviour of dynamic systems, both linear and non-linear (as in our case). Significant absorbed wave energy increases were found, both using linear models and NNs. Results were better when IMC with NNs was employed (with a nearly sixfold increase against a fivefold increase), except for the May—September period, when IMC with linear models performs better.
- Switching control of the archimedes wave swingPublication . Valério, Duarte; Costa, José Sá da; Mendes, Mário J. G. C.; Beirão, PedroControl switching is applied to the Archimedes Wave Swing (AWS), a device designed to convert the energy of sea waves into electricity. Previous simulations showed that energy production can be significantly increased using Internal Model Control, together with direct and inverse Elman Neural Network (NN) models of the AWS, and a reference based upon the phase and amplitude control strategy. Since the best performance was achieved by different NN models depending on the month of the year, further simulations were carried out showing that switching between diferente controllers, corresponding to diferente models, according to the spectrum of the incoming wave,further increases energy production.