  Online output prediction is an indispensable part of any model predictive
control implementation, especially when simplifications of the underlying
physical model have been considered and/or the operating conditions change
quite often. Furthermore, the selection of an output prediction model is
strongly related to the data available, while designing/altering the data
collection process may not be an option. Thus, in several scenarios, selecting
the most appropriate prediction model needs to be performed during runtime. To
this end, this paper introduces a supervisory output prediction scheme,
tailored specifically for input-output stable bilinear systems, that intends on
automating the process of selecting the most appropriate prediction model
during runtime. The selection process is based upon a reinforcement-learning
scheme, where prediction models are selected according to their prior
prediction performance. An additional selection process is concerned with
appropriately partitioning the control-inputs' domain in order to also allow
for switched-system approximations of the original bilinear dynamics. We show
analytically that the proposed scheme converges (in probability) to the best
model and partition. We finally demonstrate these properties through
simulations of temperature prediction in residential buildings.
