Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Petsagkourakis, Panagiotis"'
Many engineering processes can be accurately modelled using partial differential equations (PDEs), but high dimensionality and non-convexity of the resulting systems pose limitations on their efficient optimisation. In this work, a model reduction, m
Externí odkaz:
http://arxiv.org/abs/2410.11994
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 mismatch and process uncertainties, which can lead to closed-loop performa
Externí odkaz:
http://arxiv.org/abs/2211.14595
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. The interest in their application for modelling has sparked recently, spanning hybrid system identification problems and time series a
Externí odkaz:
http://arxiv.org/abs/2210.11245
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 optimization schemes and multi-fidelity Gaussian processes within a Ba
Externí odkaz:
http://arxiv.org/abs/2111.05589
Autor:
Sachio, Steven, Mowbray, Max, Papathanasiou, Maria, del Rio-Chanona, Ehecatl Antonio, Petsagkourakis, Panagiotis
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 comparing different process designs, for the comparison to be meaning
Externí odkaz:
http://arxiv.org/abs/2108.05242
A data-driven MPC scheme is proposed to safely control constrained stochastic linear systems using distributionally robust optimization. Distributionally robust constraints based on the Wasserstein metric are imposed to bound the state constraint vio
Externí odkaz:
http://arxiv.org/abs/2105.08414
Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their ability to optimize uncertain systems independently of explicit assumption o
Externí odkaz:
http://arxiv.org/abs/2104.11706
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 widely used for the purpose of improving parameter precision in nonlinear
Externí odkaz:
http://arxiv.org/abs/2011.10009
Autor:
Pan, Elton, Petsagkourakis, Panagiotis, Mowbray, Max, Zhang, Dongda, del Rio-Chanona, Antonio
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 translation to industrial practice primarily due to its inability to sat
Externí odkaz:
http://arxiv.org/abs/2011.07925
Autor:
del Rio-Chanona, Ehecatl Antonio, Petsagkourakis, Panagiotis, Bradford, Eric, Graciano, Jose Eduardo Alves, Chachuat, Benoit
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 the integration of concepts from the areas of Bayesian optimization an
Externí odkaz:
http://arxiv.org/abs/2009.08819