Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
This work addresses the need for rigorous theoretical foundations in using RNNs for control, specifically for process control applications, though it appears incremental as it builds on existing GRU and MPC methods.
The paper tackles the problem of offset-free tracking of constant references for systems modeled by GRU neural networks, proposing a Nonlinear MPC framework with guaranteed closed-loop stability, and demonstrates remarkable performance on a pH neutralization process benchmark.
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.