A pod approach to identify and control PDEs online through state dependent Riccati equations

A Alla, A Pacifico - Dynamic Games and Applications, 2024 - Springer
We address the control of partial differential equations (PDEs) with unknown parameters.
Our objective is to devise an efficient algorithm capable of both identifying and controlling …

Low-order linear parameter varying approximations for nonlinear controller design for flows

A Das, J Heiland - 2024 European Control Conference (ECC), 2024 - ieeexplore.ieee.org
The control of nonlinear large-scale dynamical models such as the incompressible Navier-
Stokes equations is a challenging task. The computational challenges in the controller …

[HTML][HTML] State Dependent Riccati for dynamic boundary control to optimize irrigation in Richards' equation framework

A Alla, M Berardi, L Saluzzi - Mathematics and Computers in Simulation, 2025 - Elsevier
We present an approach for the optimization of irrigation in a Richards' equation framework.
We introduce a proper cost functional, aimed at minimizing the amount of water provided by …

Data-driven stabilization of an oscillating flow with linear time-invariant controllers

W Jussiau, C Leclercq, F Demourant… - Journal of Fluid …, 2024 - cambridge.org
This paper presents advances towards the data-based control of periodic oscillator flows,
from their fully developed regime to their equilibrium stabilized in closed loop, with linear …

Data-driven stabilization of an oscillating flow with LTI controllers

W Jussiau, C Leclercq, F Demourant… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper presents advances towards the data-based control of periodic oscillator flows,
from their fully-developed regime to their equilibrium stabilized in closed-loop, with linear …

A feedback control strategy for optimizing irrigation in a Richards' Equation framework

A Alla, M Berardi, L Saluzzi - arxiv preprint arxiv:2407.06477, 2024 - arxiv.org
We present an approach for the optimization of irrigation in a Richards' equation framework.
We introduce a proper cost functional, aimed at minimizing the amount of water provided by …

Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design

J Heiland, Y Kim, SWR Werner - arxiv preprint arxiv:2403.18044, 2024 - arxiv.org
Polytopic autoencoders provide low-dimensional parametrizations of states in a polytope.
For nonlinear PDEs, this is readily applied to low-dimensional linear parameter-varying …

[HTML][HTML] Convolutional autoencoders, clustering, and POD for low-dimensional parametrization of flow equations

J Heiland, Y Kim - Computers & Mathematics with Applications, 2024 - Elsevier
Simulations of large-scale dynamical systems require expensive computations and large
amounts of storage. Low-dimensional representations of high-dimensional states such as in …

[PDF][PDF] Convolutional Autoencoders, Clustering and POD for Low-dimensional Parametrization of Navier-Stokes Equations

J Heiland, K Yongho - arxiv preprint arxiv:2302.01278, 2024 - pure.mpg.de
Simulations of large-scale dynamical systems require expensive computations. Low-
dimensional parametrization of high-dimensional states such as Proper Orthogonal …