| Data-driven distributionally robust MPC using the Wasserstein metric Z Zhong, EA del Rio-Chanona, P Petsagkourakis arXiv preprint arXiv:2105.08414, 2021 | 14 | 2021 |
| A hybrid data-driven and mechanistic model soft sensor for estimating CO2 concentrations for a carbon capture pilot plant Y Zhuang, Y Liu, A Ahmed, Z Zhong, EA del Rio Chanona, CP Hale, ... Computers in Industry 143, 103747, 2022 | 11 | 2022 |
| Tube-based distributionally robust model predictive control for nonlinear process systems via linearization Z Zhong, EA del Rio-Chanona, P Petsagkourakis Computers & Chemical Engineering 170, 108112, 2023 | 8 | 2023 |
| Nonlinear Wasserstein Distributionally Robust Optimal Control Z Zhong, JJ Zhu ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems …, 2023 | 3 | 2023 |
| An efficient data-driven distributionally robust MPC leveraging linear programming Z Zhong, EA Del Rio-Chanona, P Petsagkourakis 2023 American Control Conference (ACC), 2022-2027, 2023 | 3 | 2023 |
| Distributionally Robust MPC for Nonlinear Systems Z Zhong, EA del Rio-Chanona, P Petsagkourakis IFAC-PapersOnLine 55 (7), 606-613, 2022 | 2 | 2022 |
| Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddings O Schön, Z Zhong, S Soudjani arXiv preprint arXiv:2403.10497, 2024 | 1 | 2024 |
| Multi-level Optimal Control with Neural Surrogate Models D Kalise, E Loayza-Romero, KA Morris, Z Zhong arXiv preprint arXiv:2402.07763, 2024 | | 2024 |
Ehecatl Antonio del Río ChanonaSargent Centre for Process Systems Engineering, Imperial College LondonDirección de correo verificada de imperial.ac.uk
