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.

翻译：利用经常性神经网络进行系统识别最近引起越来越多的关注,这得益于其黑箱模型能力。 尽管许多应用软件都采用了成果丰硕的网络,但用于提供严格理论基础以证明其用于控制目的的作品很少。本文的目的是说明如何在非线性MPC框架内培训和使用稳定的Gated经常单位(GRUs),即特定的RNN结构,以便对经常引用进行无偏向的跟踪,保证封闭环稳定性。拟议的方法在pH值的中性进程基准上进行了测试,显示了显著的绩效。