Jing Xie

PhD Candidate in Information Technology – Systems and Control

Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)

Politecnico di Milano

Jing Xie graduated with a master’s degree in electrical engineering with a focus on automation and robotics at Technical University of Munich. He did an internship at Robert Bosch on in-car monitoring system using machine learning methods in Hildesheim. His master thesis was on tube-based incremental model predictive control for robot manipulators. His main research interest is learning-based MPC.

Project description

Machine Learning (ML) techniques, and in particular Recurrent Neural Networks (RNN), are gaining a wide popularity in the control community in view of their ability to obtain, from plant data, models of complex dynamic systems, characterized by a strong nonlinear behavior. However, theoretical results related to control design methods, and in particular Model Predictive Control (MPC) based on RNN, are still needed and fundamental issues must be considered. Jing Xie focuses on developing learning-based control algorithms for industrial systems.



Xie, Jing; Bonassi, Fabio; Scattolini, Riccardo

Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models Working paper

2024, (Submitted to IEEE Transactions on Automation Science and Engineering).

Abstract | Links | BibTeX


Xie, Jing; Simpson, Léo; Asprion, Jonas; Scattolini, Riccardo

A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units Working paper

2024, (Submitted to 2024 IEEE Conference on Control Technology and Applications).

Abstract | Links | BibTeX


Xie, Jing; Bonassi, Fabio; Farina, Marcello; Scattolini, Riccardo

Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models Journal Article

In: International Journal of Robust and Nonlinear Control, 2023.

Abstract | Links | BibTeX


Bonassi, Fabio; Farina, Marcello; Xie, Jing; Scattolini, Riccardo

An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models Proceedings Article

In: 2022 IEEE 61st Conference on Decision and Control (CDC), pp. 2123-2128, IEEE, 2022, ISBN: 978-1-6654-6761-2.

Abstract | Links | BibTeX


Bonassi, Fabio; Farina, Marcello; Xie, Jing; Scattolini, Riccardo

On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments Journal Article

In: Journal of Process Control, vol. 114, pp. 92-104, 2022, ISSN: 0959-1524.

Abstract | Links | BibTeX


Bonassi, Fabio; Xie, Jing; Farina, Marcello; Scattolini, Riccardo

Towards lifelong learning of Recurrent Neural Networks for control design Proceedings Article

In: 2022 European Control Conference (ECC), pp. 2018–2023, IEEE 2022.