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

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.

arXiv commentThis 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

References in corpus (4)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• On the stability properties of Gated Recurrent Units neural networks
• Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
• Model predictive control design for dynamical systems learned by Long Short-Term Memory Networks

Cited by in corpus (6)

• On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments
• Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
• Stability of discrete-time feed-forward neural networks in NARX configuration
• Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems
• An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
• Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models