  Data-driven control offers a viable option for control scenarios where
constructing a system model is expensive or time-consuming. Nonetheless, many
of these algorithms are not entirely automated, often necessitating the
adjustment of multiple hyperparameters through cumbersome trial-and-error
processes and demanding significant amounts of data. In this paper, we explore
a meta-learning approach to leverage potentially existing prior knowledge about
analogous (though not identical) systems, aiming to reduce both the
experimental workload and ease the tuning of the available degrees of freedom.
We validate this methodology through an experimental case study involving the
tuning of proportional, integral (PI) controllers for brushless DC (BLDC)
motors with variable loads and architectures.
