14 citations · 20 across the 6 of their papers we have counts for
11 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…
Model reduction, machine learning based global optimisation for large-scale steady state nonlinear systems
Min Tao, Panagiotis Petsagkourakis, Jie Li +1
Many engineering processes can be accurately modelled using partial differential equations (PDEs), but high dimensionality and non-convexity of the resulting systems pose limitatio…
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…
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Ilya Orson Sandoval, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. The interest in their application for modelling has sparked rece…
Integrating process design and control using reinforcement learning
Steven Sachio, Max Mowbray, Maria Papathanasiou +2
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When…
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…