{"id":680,"date":"2021-01-16T20:27:53","date_gmt":"2021-01-16T20:27:53","guid":{"rendered":"https:\/\/elo-x.eu\/?p=680"},"modified":"2023-09-21T15:16:55","modified_gmt":"2023-09-21T15:16:55","slug":"jing-xie","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=680","title":{"rendered":"Jing Xie"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tJing Xie \n\t\t\t<\/h2 > \n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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PhD Candidate in Information Technology \u2013 Systems and Control<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)<\/b><\/span><\/p>

Politecnico di Milano<\/b><\/span><\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Jing Xie graduated with a master\u2019s 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead more about this project<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1.<\/div>

Simpson, L\u00e9o; Xie, Jing; Asprion, Jonas; Scattolini, Riccardo<\/p>

A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units<\/a> Proceedings Article<\/span> <\/p>

In: <\/span>2024 IEEE Conference on Control Technology and Applications (CCTA), <\/span>pp. 675-680, <\/span>IEEE, <\/span>Newcastle upon Tyne, United Kingdom, <\/span>2024<\/span>, ISSN: 2768-0770<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@inproceedings{xie2024learningbased,
\r\ntitle = {A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units},
\r\nauthor = {L\u00e9o Simpson and Jing Xie and Jonas Asprion and Riccardo Scattolini
\r\n},
\r\nurl = {https:\/\/arxiv.org\/abs\/2402.05606},
\r\ndoi = {10.1109\/CCTA60707.2024.10666571},
\r\nissn = {2768-0770},
\r\nyear  = {2024},
\r\ndate = {2024-09-11},
\r\nurldate = {2024-09-11},
\r\nbooktitle = {2024 IEEE Conference on Control Technology and Applications (CCTA)},
\r\npages = {675-680},
\r\npublisher = {IEEE},
\r\naddress = {Newcastle upon Tyne, United Kingdom},
\r\nabstract = {Temperature control is a complex task due to its often unknown dynamics and disturbances. This paper explores the use of Neural Nonlinear AutoRegressive eXogenous (NNARX) models for nonlinear system identification and model predictive control of a temperature control unit. First, the NNARX model is identified from input-output data collected from the real plant, and a state-space representation with known measurable states consisting of past input and output variables is formulated. Second, a tailored model predictive controller is designed based on the trained NNARX network. The proposed control architecture is experimentally tested on the temperature control units manufactured by Tool-Temp AG. The results achieved are compared with those obtained using a PI controller and a linear MPC. The findings illustrate that the proposed scheme achieves satisfactory tracking performance while incurring the lowest energy cost among the compared controllers.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Temperature control is a complex task due to its often unknown dynamics and disturbances. This paper explores the use of Neural Nonlinear AutoRegressive eXogenous (NNARX) models for nonlinear system identification and model predictive control of a temperature control unit. First, the NNARX model is identified from input-output data collected from the real plant, and a state-space representation with known measurable states consisting of past input and output variables is formulated. Second, a tailored model predictive controller is designed based on the trained NNARX network. The proposed control architecture is experimentally tested on the temperature control units manufactured by Tool-Temp AG. The results achieved are compared with those obtained using a PI controller and a linear MPC. The findings illustrate that the proposed scheme achieves satisfactory tracking performance while incurring the lowest energy cost among the compared controllers.<\/div>
Close<\/a><\/p><\/div>