Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks. Issue 14 (2021)

• Record Type: Journal Article
• Authors: Bonassi, Fabio; da Silva, Caio Fabio Oliveira; Scattolini, Riccardo
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 14(2021)
• Page Start: 54
• Page End: 59
• Publication Date: 2021
• Subjects: Machine Learning -- Nonlinear Model Predictive Control -- Model -- Identification of Nonlinear Systems -- Offset-free Tracking; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.10.328
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.