Sergio Lucia

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Sebastian EngellProfessor for Process Dynamics and Operations, TU Dortmund, Germany and Chair of the ASPIRE APGVerified email at tu-dortmund.de
Rolf FindeisenTU DarmstadtVerified email at iat.tu-darmstadt.de
Benjamin KargTU Dortmund, Laboratory of Process Automation SystemsVerified email at tu-dortmund.de
Sankaranarayanan SubramanianAPC Engineer at INEOSVerified email at ineos.com
Radoslav PaulenSlovak University of Technology in BratislavaVerified email at stuba.sk
Felix FiedlerTechnische Universität DortmundVerified email at tu-dortmund.de
Óscar LucíaIEEE Fellow. Full Professor. I3A, Universidad de Zaragoza. Zaragoza, Spain.Verified email at unizar.es
Richard D. BraatzEdwin R. Gilliland Professor, Massachusetts Institute of TechnologyVerified email at mit.edu
Hector SarnagoUniversidad de ZaragozaVerified email at unizar.es
Moritz DiehlProfessor of Systems, Control and Optimization at the University of FreiburgVerified email at imtek.uni-freiburg.de
Peter NeubauerTU Berlin, Professor of Bioprocess EngineeringVerified email at tu-berlin.de
M. Nicolas Cruz BournazouKIWI-biolab, Chair of Bioprocess Engineering Technical University BerlinVerified email at mailbox.tu-berlin.de
Teodoro AlamoUniversity of SevillaVerified email at us.es
Lukas LükenPhD Student, TU Dortmund UniversityVerified email at tu-dortmund.de
Moritz HeinleinProcess Automation SystemsVerified email at tu-dortmund.de
Niels KrauschPostdoc, NovartisVerified email at novartis.com
Joel A. E. AnderssonUniversity of Wisconsin - MadisonVerified email at wisc.edu
Sebastian AlbrechtSiemens AGVerified email at siemens.com
Yiming WanHuazhong University of Science and TechnologyVerified email at hust.edu.cn
Dean BrandnerPhD student, Technische Universität DortmundVerified email at tu-dortmund.de

Sergio Lucia

Full Professor, Chair of Process Automation Systems, TU Dortmund

Verified email at tu-dortmund.de - Homepage

Model Predictive Control Optimization Machine Learning Process Control Reinforcement Learning


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Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty S Lucia, T Finkler, S Engell Journal of process control 23 (9), 1306-1319, 2013	374	2013
Efficient representation and approximation of model predictive control laws via deep learning B Karg, S Lucia IEEE Transactions on Cybernetics 50 (9), 3866-3878, 2020	269	2020
Handling uncertainty in economic nonlinear model predictive control: A comparative case study S Lucia, JAE Andersson, H Brandt, M Diehl, S Engell Journal of Process Control 24 (8), 1247–1259, 2014	191	2014
Rapid development of modular and sustainable nonlinear model predictive control solutions S Lucia, A Tătulea-Codrean, C Schoppmeyer, S Engell Control Engineering Practice 60, 51-62, 2017	157	2017
Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High Level Synthesis Tool S Lucia, D Navarro, O Lucia, P Zometa, R Findeisen IEEE Transactions on Industrial Informatics 14 (1), 137 - 145, 2018	123	2018
A deep learning-based approach to robust nonlinear model predictive control S Lucia, B Karg Proc. of the 6th IFAC Conference on Nonlinear Model Predictive Control, 610-615, 2018	109	2018
Deep learning-based model predictive control for resonant power converters S Lucia, D Navarro, B Karg, H Sarnago, O Lucia IEEE Transactions on Industrial Informatics 17 (1), 409-420, 2020	102	2020
A new robust NMPC scheme and its application to a semi-batch reactor example S Lucia, TF Finkler, D Basak, S Engell Proceedings of the International Symposium on Advanced Control of Chemical …, 2012	80	2012
Predictive control, embedded cyberphysical systems and systems of systems–A perspective S Lucia, M Kögel, P Zometa, DE Quevedo, R Findeisen Annual Reviews in Control 41, 193-207, 2016	79	2016
Fusion of machine learning and MPC under uncertainty: What advances are on the horizon? A Mesbah, KP Wabersich, AP Schoellig, MN Zeilinger, S Lucia, ... 2022 American Control Conference (ACC), 342-357, 2022	64	2022
Contract-based predictive control of distributed systems with plug and play capabilities S Lucia, M Kögel, R Findeisen IFAC-PapersOnLine 48 (23), 205-211, 2015	62	2015
Probabilistic performance validation of deep learning‐based robust NMPC controllers B Karg, T Alamo, S Lucia International Journal of Robust and Nonlinear Control 31 (18), 8855-8876, 2021	60	2021
Deep learning-based embedded mixed integer model predictive control B Karg, S Lucia Proc. of the European Control Conference, 2075-2080, 2018	56	2018
do-mpc: Towards FAIR nonlinear and robust model predictive control F Fiedler, B Karg, L Lüken, D Brandner, M Heinlein, F Brabender, S Lucia Control Engineering Practice 140, 105676, 2023	53	2023
Improving scenario decomposition algorithms for robust nonlinear model predictive control R Marti, S Lucia, D Sarabia, R Paulen, S Engell, C de Prada Computers & Chemical Engineering 79, 30-45, 2015	53	2015
On the relationship between data-enabled predictive control and subspace predictive control F Fiedler, S Lucia 2021 European Control Conference (ECC), 222-229, 2021	52	2021
Stability and feasibility of neural network-based controllers via output range analysis B Karg, S Lucia 2020 59th IEEE Conference on Decision and Control (CDC), 4947-4954, 2020	50	2020
Dual robust nonlinear model predictive control: A multi-stage approach S Thangavel, S Lucia, R Paulen, S Engell Journal of process control 72, 39-51, 2018	46	2018
Robust Multi-stage Nonlinear Model Predictive Control S Lucia TU Dortmund, 2014	45	2014
Robust nonlinear model predictive control with reduction of uncertainty via robust optimal experiment design S Lucia, R Paulen IFAC Proceedings Volumes 47 (3), 1904-1909, 2014	44	2014

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