Model- and data-driven Control

This project combines data- and model-driven predictive controllers to solve problems that deal with unknown functions operating interdependently with functions that are explicitly. In other words, we are combining black-box machine learning techniques with analytical approaches based on 1st-principle physical models.

  • our algorithms can cope with very little data and build solutions based on ‘‘learning’’ from the recently generated trajectories
  • in other words, it uses recursive approaches that are guaranteed to improve performance in each iteration until converging to an optimal trajectory, typically needing only a few iterations.