文章基本信息

标题：Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
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作者：Fabio Bonassi ; Caio Fabio ; Oliveira da Silva 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：14
页码：54-59
DOI：10.1016/j.ifacol.2021.10.328
语种：English
出版社：Elsevier
摘要：AbstractThe 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.
关键词：KeywordsMachine LearningNonlinear Model Predictive ControlModelIdentification of Nonlinear SystemsOffset-free Tracking