{"id":726,"date":"2021-08-29T13:03:41","date_gmt":"2021-08-29T13:03:41","guid":{"rendered":"https:\/\/elo-x.eu\/?p=726"},"modified":"2024-05-28T13:12:33","modified_gmt":"2024-05-28T13:12:33","slug":"leo-simpson","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=726","title":{"rendered":"L\u00e9o Simpson"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tL\u00e9o Simpson \n\t\t\t<\/h2 > \n\t\t\t\t\t\t\t\t\t<\/a>\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 Mathematics at Tool-Temp AG and at the University of Freiburg<\/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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Tool-Temp AG<\/b><\/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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Leo Simpson was born Toulouse, France, in 1996. After studying scientific topics during two years of “Classe Pr\u00e9paratoire”, he studied at Ecole Polytechnique from 2016 to 2019, for a master of applied mathematics. During these studies, he has been focusing on mathematics, and more, in a multidisciplinary formation involving physics, mechanics, chemistry, and engineering sciences.\u00a0<\/p>

He pursued his studies doing a master of mathematics at the Technical University of Munich from 2019 to 2021. This master’s degree was focused on optimization, and his master thesis topic was numerical optimal control applied to walking robots, in a group of researchers from Siemens Technology. He then decided to start his Ph.D. in Tool-temp, under the supervision of Dr. Jonas Asprion and Prof. Moritz Diehl, in the framework of the Marie Curie Initial Training Network \u201cELO-X\u201d.<\/p>

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Tool-temp is a company producing Temperature Control Units, tools that control the temperature of a variety of industrial processes by means of circulating a thermal fluid. The dynamics of temperature transfers involved in these processes can be quite complex and are hard to model as they strongly depend on unknown parameters of the industrial process. The aim of this Ph.D. project is to investigate algorithms which simultaneously learn the parameters of these dynamics and perform a suitable control policy to control the temperature over these dynamics, using a Model Predictive Control (MPC) scheme.<\/p>

Considering that other control applications might also benefit from general tools for parametric system identification, the development of efficient and reliable algorithms for parametric system identification will be central to this project.<\/p>

Both numerical and real machine experiments are considered, to assess the quality of different algorithms.<\/p>

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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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Publications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\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>