Jan Drgona
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All you need to know about model predictive control for buildings
J Drgoňa, J Arroyo, IC Figueroa, D Blum, K Arendt, D Kim, EP Ollé, ...
Annual Reviews in Control 50, 190-232, 2020
Approximate model predictive building control via machine learning
J Drgoňa, D Picard, M Kvasnica, L Helsen
Applied Energy 218, 199-216, 2018
Physics-constrained deep learning of multi-zone building thermal dynamics
J Drgoňa, AR Tuor, V Chandan, DL Vrabie
Energy and Buildings 243, 110992, 2021
Impact of the controller model complexity on model predictive control performance for buildings
D Picard, J Drgoňa, M Kvasnica, L Helsen
Energy and Buildings 152, 739-751, 2017
Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration
J Drgoňa, D Picard, L Helsen
Journal of Process Control 88, 63-77, 2020
Building optimization testing framework (BOPTEST) for simulation-based benchmarking of control strategies in buildings
D Blum, J Arroyo, S Huang, J Drgoňa, F Jorissen, HT Walnum, Y Chen, ...
Journal of Building Performance Simulation 14 (5), 586-610, 2021
Optimal control of a laboratory binary distillation column via regionless explicit MPC
J Drgoňa, M Klaučo, F Janeček, M Kvasnica
Computers & Chemical Engineering 96, 139-148, 2017
Explicit Stochastic MPC Approach to Building Temperature Control
J. Drgoňa, – M. Kvasnica, – M. Klaučo, – M. Fikar
V IEEE Conference on Decision and Control, 6440–6445, 2013
Constrained neural ordinary differential equations with stability guarantees
A Tuor, J Drgona, D Vrabie
arXiv preprint arXiv:2004.10883, 2020
Constructing neural network based models for simulating dynamical systems
C Legaard, T Schranz, G Schweiger, J Drgoňa, B Falay, C Gomes, ...
ACM Computing Surveys 55 (11), 1-34, 2023
MPC-based reference governor control of a continuous stirred-tank reactor
J Holaza, M Klaučo, J Drgoňa, J Oravec, M Kvasnica, M Fikar
Computers & Chemical Engineering 108, 289-299, 2018
Differentiable predictive control: Deep learning alternative to explicit model predictive control for unknown nonlinear systems
J Drgoňa, K Kiš, A Tuor, D Vrabie, M Klaučo
Journal of Process Control 116, 80-92, 2022
Comparison of MPC Strategies for Building Control.
M Drgoňa, J. – Kvasnica
V Proceedings of the 19th International Conference on Process Control, 401–406, 2013
Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees
J Drgona, A Tuor, D Vrabie
arXiv preprint arXiv:2004.11184, 2020
Building Temperature Control by Simple MPC-like Feedback Laws Learned from Closed-Loop Data.
S Klaučo, M. – Drgoňa, J. – Kvasnica, M. – Di Cairano
V Preprints of the 19th IFAC World Congress Cape Town (South Africa), 581–586, 2014
Constrained block nonlinear neural dynamical models
E Skomski, S Vasisht, C Wight, A Tuor, J Drgoňa, D Vrabie
2021 American Control Conference (ACC), 3993-4000, 2021
A guideline to document occupant behavior models for advanced building controls
B Dong, R Markovic, S Carlucci, Y Liu, A Wagner, A Liguori, C van Treeck, ...
Building and Environment 219, 109195, 2022
MPC-based reference governors for thermostatically controlled residential buildings
J Drgoňa, M Klaučo, M Kvasnica
2015 54th IEEE conference on decision and control (CDC), 1334-1339, 2015
Deep learning explicit differentiable predictive control laws for buildings
J Drgoňa, A Tuor, E Skomski, S Vasisht, D Vrabie
IFAC-PapersOnLine 54 (6), 14-19, 2021
NeuroMANCER: Neural modules with adaptive nonlinear constraints and efficient regularizations
A Tuor, J Drgona, E Skomski, J Koch, Z Chen, D Vrabie
URL https://github. com/pnnl/neuromancer, 2021
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