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.

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

Fabio Bonassi;Caio Fabio Oliveira da Silva;Riccardo Scattolini
2021-01-01

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.
2021
Proceedings of the 3rd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2021
Machine Learning, Nonlinear Model Predictive Control, Model Identification of Nonlinear Systems, Offset-free Tracking
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1198857
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