SOURCE-LINKED INTELLIGENCE

Smart Wave Energy Conversion via Learning and Low-Cost Control

CORDIS · observation · Publication date unknown

els in the controller design. To overcome this, SWELL will develop the first data-driven model predictive controller (MPC) tailored to WECs, combining cutting-edge control methods, hydrodynamics, and machine learning. SWELL will deliver accurate and validated models capturing the ubiquitous nonlinear effects of WECs and exploit them in the design of an MPC that optimizes energy conversion. By integrating expertise from three world-class hosts, the unique nature of SWELL will enable efficient, fast, and practical control implementation with real-time capabilities and a low-cost design, supporting the pathway towards the effective commercialization of wave energy. This project comprises four scientific work packages, which accomplish: (i) accurate nonlinear hydrodynamic modeling of wave energy converters, (ii) efficient MPC design exploitating the explicit MPC paradigm enabled by convex relaxation techniques, (iii) experimental validation of the developed models and control, through the definition of a custom analog electronic circuit efficiently implementing the designed MPC, and (iv)

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recordType

award

status

SIGNED

region

EU

value

332913.72

unit

EUR