  When models are inaccurate, the performance of model-based control will
degrade. For linear quadratic control, an event-triggered learning framework is
proposed that automatically detects inaccurate models and triggers the learning
of a new process model when needed. This is achieved by analyzing the
probability distribution of the linear quadratic cost and designing a learning
trigger that leverages Chernoff bounds. In particular, whenever empirically
observed cost signals are located outside the derived confidence intervals, we
can provably guarantee that this is with high probability due to a model
mismatch. With the aid of numerical and hardware experiments, we demonstrate
that the proposed bounds are tight and that the event-triggered learning
algorithm effectively distinguishes between inaccurate models and probabilistic
effects such as process noise. Thus, a structured approach is obtained that
decides when model learning is beneficial.
