Direct data-driven controller design from bounded errors-in-variables measurements

My research focuses on control, optimization, and learning for dynamical systems.

I coordinate SWELL, Smart Wave Energy Conversion via Learning and Low-Cost Control, a Marie Sklodowska-Curie Global Postdoctoral Fellowship project developing data-driven model predictive control for wave energy converters.

The project combines nonlinear hydrodynamic and data-driven modeling, efficient MPC design, experimental validation, and open-source software to help make wave energy more competitive.