Sergio Lucia
Sergio Lucia
Full Professor, Chair of Process Automation Systems, TU Dortmund
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
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
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
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
Deep learning-based embedded mixed integer model predictive control
B Karg, S Lucia
Proc. of the European Control Conference, 2075-2080, 2018
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
On the relationship between data-enabled predictive control and subspace predictive control
F Fiedler, S Lucia
2021 European Control Conference (ECC), 222-229, 2021
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
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
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
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
Robust Multi-stage Nonlinear Model Predictive Control
S Lucia
TU Dortmund, 2014
Robust Nonlinear Model Predictive Control of a Batch Bioreactor Using Multi-stage Stochastic Programming
S Lucia, S Engell
European Control Conference (ECC 2013), 4124-4129, 2013
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