  Automating the calibration of the parameters of a control policy by means of
global optimization requires quantifying a closed-loop performance function. As
this can be impractical in many situations, in this paper we suggest a
semi-automated calibration approach that requires instead a human calibrator to
express a preference on whether a certain control policy is "better" than
another one, therefore eliminating the need of an explicit performance index.
In particular, we focus our attention on semi-automated calibration of Model
Predictive Controllers (MPCs), for which we attempt computing the set of best
calibration parameters by employing the recently-developed active
preference-based optimization algorithm GLISp. Based on the preferences
expressed by the human operator, GLISp learns a surrogate of the underlying
closed-loop performance index that the calibrator (unconsciously) uses and
proposes, iteratively, a new set of calibration parameters to him or her for
testing and for comparison against previous experimental results. The resulting
semi-automated calibration procedure is tested on two case studies, showing the
capabilities of the approach in achieving near-optimal performance within a
limited number of experiments.
