  The use of Recurrent Neural Networks (RNNs) for system identification has
recently gathered increasing attention, thanks to their black-box modeling
capabilities.Albeit RNNs have been fruitfully adopted in many applications,
only few works are devoted to provide rigorous theoretical foundations that
justify their use for control purposes. The aim of this paper is to describe
how stable Gated Recurrent Units (GRUs), a particular RNN architecture, can be
trained and employed in a Nonlinear MPC framework to perform offset-free
tracking of constant references with guaranteed closed-loop stability. The
proposed approach is tested on a pH neutralization process benchmark, showing
remarkable performances.
