14 citations · 15 across the 5 of their papers we have counts for
5 papers
Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributional…
Multi-level Optimal Control with Neural Surrogate Models
Dante Kalise, Estefanía Loayza-Romero, Kirsten A. Morris +1
Optimal actuator and control design is studied as a multi-level optimisation problem, where the actuator design is evaluated based on the performance of the associated optimal clos…
Nonlinear Wasserstein Distributionally Robust Optimal Control
Zhengang Zhong, Jia-Jie Zhu
This paper presents a novel approach to addressing the distributionally robust nonlinear model predictive control (DRNMPC) problem. Current literature primarily focuses on the stat…
Tube-based Distributionally Robust Model Predictive Control for Nonlinear Process Systems via Linearization
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic models are however affected by plant-model mism…
Data-driven distributionally robust MPC using the Wasserstein metric
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
A data-driven MPC scheme is proposed to safely control constrained stochastic linear systems using distributionally robust optimization. Distributionally robust constraints based o…