Leonardo Gambarelli
On the viability and effectiveness of Reinforcement Learning techniques for Wave-Energy converter control.
Rel. Giuliana Mattiazzo, Edoardo Pasta, Sergej Antonello Sirigu. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022
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Abstract
An application of Reinforcement Learning (RL) techniques, a type of Machine Learning algorithm inspired by the human learning (through positive and negative rewards), is investigated for the problem of wave-energy convertes (WECs) control. RL techniques seems particularly suited for the WECs' control problem due to their model-free nature and their intrinsic definition as a Markov Decision Process, which should be a good way to cast the WECs' situations. Wave energy is an interesting source given its high energy potential, which is almost entirely untouched by men (it is estimated that being able to harvest 0.2% of the energy of the seas on Earth, would provide energy worldwide).
The main difficulty in making sea energy viable is its control problem: an optimal control is needed for its economical viability, but usually this optimal control is formulated by exploiting control oriented model for the WECs, however those models are affected by strong uncertainties that could results in a suboptimal control
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