  Model-based algorithms are deeply rooted in modern control and systems
theory. However, they usually come with a critical assumption - access to an
accurate model of the system. In practice, models are far from perfect. Even
precisely tuned estimates of unknown parameters will deteriorate over time.
Therefore, it is essential to detect the change to avoid suboptimal or even
dangerous behavior of a control system. We propose to combine statistical tests
with dedicated parameter filters that track unknown system parameters from
state data. These filters yield point estimates of the unknown parameters and,
further, an inherent notion of uncertainty. When the point estimate leaves the
confidence region, we trigger active learning experiments. We update models
only after enforcing a sufficiently small uncertainty in the filter. Thus,
models are only updated when necessary and statistically significant while
ensuring guaranteed improvement, which we call event-triggered learning. We
validate the proposed method in numerical simulations of a DC motor in
combination with model predictive control.
