  The online implementation of model predictive control for constrained
multivariate systems has two main disadvantages: it requires an estimate of the
entire model state and an optimisation problem must be solved online. These
issues have typically been treated separately. This work proposes an integrated
approach for the offline training of an output feedback neural network
controller in closed loop. Online this neural network controller computers the
plant inputs cheaply using noisy measurements. In addition, the controller can
be trained to only make use of certain predefined measurements. Further, a
heuristic approach is proposed to perform the automatic selection of important
measurements. The proposed method is demonstrated by extensive simulations
using a non-linear distillation column model of 50 states.
