| 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 | 334 | 2020 |
| Approximate model predictive building control via machine learning J Drgoňa, D Picard, M Kvasnica, L Helsen Applied Energy 218, 199-216, 2018 | 215 | 2018 |
| 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 | 63 | 2017 |
| Physics-constrained deep learning of multi-zone building thermal dynamics J Drgoňa, AR Tuor, V Chandan, DL Vrabie Energy and Buildings 243, 110992, 2021 | 57 | 2021 |
| 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 | 56 | 2020 |
| 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 | 36 | 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 | 36* | 2013 |
| 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 | 33 | 2021 |
| Constrained neural ordinary differential equations with stability guarantees A Tuor, J Drgona, D Vrabie arXiv preprint arXiv:2004.10883, 2020 | 31 | 2020 |
| 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 | 26 | 2018 |
| Comparison of MPC Strategies for Building Control. M Drgoňa, J. – Kvasnica V Proceedings of the 19th International Conference on Process Control, 401–406, 2013 | 23* | 2013 |
| 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 | 22* | 2014 |
| Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees J Drgona, A Tuor, D Vrabie arXiv preprint arXiv:2004.11184, 2020 | 19* | 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 | 17 | 2023 |
| 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 | 16 | 2021 |
| 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 | 15* | 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 | 15 | 2015 |
| NeuroMANCER: Neural modules with adaptive nonlinear constraints and efficient regularizations A Tuor, J Drgona, E Skomski URL https://github. com/pnnl/neuromancer, 2021 | 11 | 2021 |
| 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 | 11 | 2021 |
| Offset-free hybrid model predictive control of bispectral index in anesthesia D Ingole, J Drgoňa, M Kvasnica 2017 21st International Conference on Process Control (PC), 422-427, 2017 | 10 | 2017 |
Draguna VrabiePacific Northwest National LaboratoryOverená e-mailová adresa na: pnnl.gov
