| 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 | 719 | 2020 |
| Approximate model predictive building control via machine learning J Drgoňa, D Picard, M Kvasnica, L Helsen Applied Energy 218, 199-216, 2018 | 290 | 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 | 133 | 2021 |
| 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 | 111 | 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 | 110 | 2020 |
| 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 | 99 | 2017 |
| 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 | 83 | 2023 |
| 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 | 62* | 2022 |
| Ten questions concerning reinforcement learning for building energy management Z Nagy, G Henze, S Dey, J Arroyo, L Helsen, X Zhang, B Chen, ... Building and Environment 241, 110435, 2023 | 54 | 2023 |
| 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 | 40 | 2017 |
| Physics-informed machine learning for modeling and control of dynamical systems TX Nghiem, J Drgoňa, C Jones, Z Nagy, R Schwan, B Dey, A Chakrabarty, ... 2023 American Control Conference (ACC), 3735-3750, 2023 | 38 | 2023 |
| Constrained neural ordinary differential equations with stability guarantees A Tuor, J Drgona, D Vrabie arXiv preprint arXiv:2004.10883, 2020 | 37 | 2020 |
| 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 | 35* | 2013 |
| Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees J Drgona, A Tuor, D Vrabie arXiv preprint arXiv:2004.11184, 2020 | 34* | 2020 |
| 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 | 31 | 2022 |
| Neural ordinary differential equations for nonlinear system identification A Rahman, J Drgoňa, A Tuor, J Strube 2022 American Control Conference (ACC), 3979-3984, 2022 | 30 | 2022 |
| 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 | 30 | 2021 |
| 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 | 30 | 2018 |
| 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 | 27* | 2014 |
| Comparison of MPC Strategies for Building Control. M Drgoňa, J. – Kvasnica V Proceedings of the 19th International Conference on Process Control, 401–406, 2013 | 27* | 2013 |
Draguna VrabiePacific Northwest National LaboratoryVerified email at pnnl.gov
