Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks

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 provide rigorous theoretical foundations that justify control purposes. aim this paper is describe how stable Gated Units (GRUs), a particular RNN architecture, can be trained and employed Nonlinear MPC framework perform offset-free tracking constant references with guaranteed closed-loop stability. proposed approach tested on pH neutralization process benchmark, showing remarkable performances.