  Many control tasks can be formulated as a tracking problem of a known or
unknown reference signal. Examples are movement compensation in collaborative
robotics, the synchronisation of oscillations for power systems or reference
tracking of recipes in chemical process operation. Tracking performance as well
as guaranteeing stability of the closed loop strongly depends on two factors:
Firstly, it depends on whether the future desired tracking reference signal is
known and, secondly, whether the system can track the reference at all. This
paper shows how to use machine learning, i.e. Gaussian processes, to learn a
reference from (noisy) data, while guaranteeing trackability of the modified
desired reference predictions in the framework of model predictive control.
Guarantees are provided by adjusting the hyperparameters via a constrained
optimization. Two specific scenarios, i.e. asymptotically constant and
periodical references, are discussed.
