14 citations · 25 across the 12 of their papers we have counts for
16 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…
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes
Panagiotis Petsagkourakis, Benoit Chachuat, Ehecatl Antonio del Rio-Chanona
This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free…
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…
Safe Chance Constrained Reinforcement Learning for Batch Process Control
Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Río Chanona +1
Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their abilit…
Safe model-based design of experiments using Gaussian processes
Panagiotis Petsagkourakis, Federico Galvanin
Construction of kinetic models has become an indispensable step in the development and scale up of processes in the industry. Model-based design of experiments (MBDoE) has been wid…
Constrained Model-Free Reinforcement Learning for Process Optimization
Elton Pan, Panagiotis Petsagkourakis, Max Mowbray +2
Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked tr…
Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation
Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford +2
This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in…
Chance Constrained Policy Optimization for Process Control and Optimization
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +3
Chemical process optimization and control are affected by 1) plant-model mismatch, 2) process disturbances, and 3) constraints for safe operation. Reinforcement learning by policy…
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +2
Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real time. The main bottleneck in the industrial a…
Reinforcement Learning for Batch Bioprocess Optimization
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +2
Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to thei…
Input-Output Stability of Barrier-Based Model Predictive Control
Panagiotis Petsagkourakis, William P. Heath, Joaquin Carrasco +1
Conditions for input-output stability of barrier-based model predictive control of linear systems with linear and convex nonlinear (hard or soft) constraints are established throug…
Stability Analysis of Piecewise Affine Systems with Multi-model Model Predictive Control
Panagiotis Petsagkourakis, William P. Heath, Constantinos Theodoropoulos
Constrained model predictive control (MPC) is a widely used control strategy, which employs moving horizon-based on-line optimisation to compute the optimum path of the manipulated…