  This paper develops a control scheme, based on the use of Long Short-Term
Memory neural network models and Nonlinear Model Predictive Control, which
guarantees recursive feasibility with slow time variant set-points and
disturbances, input and output constraints and unmeasurable state. Moreover, if
the set-point and the disturbance are asymptotically constant, offset-free
tracking is guaranteed. Offset-free tracking is obtained by augmenting the
model with a disturbance, to be estimated together with the states of the Long
Short-Term Memory network model by a properly designed observer. Satisfaction
of the output constraints in presence of observer estimation error, time
variant set-points and disturbances is obtained using a constraint tightening
approach.
