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

Autor:	Bonassi, Fabio, da Silva, C. F. Oliveira, Scattolini, Riccardo
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1016/j.ifacol.2021.10.328
Popis:	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. Comment: This work is the extended version of the article accepted at the Third IFAC Conference on Modelling, Identification and Control of Nonlinear Systems (MICNON 2021) for publication under a Creative Commons Licence CC-BY-NC-ND
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.02383 Zobrazit plný text záznamu View this record from Arxiv