1 code implementation • 1 May 2024 • Luca Furieri, Clara Lucía Galimberti, Giancarlo Ferrari-Trecate
The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms.
1 code implementation • 26 Mar 2024 • Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause, Giancarlo Ferrari-Trecate
Based on these bounds, we propose a new method for designing optimal controllers, offering a principled way to incorporate prior knowledge into the synthesis process, which aids in improving the control policy and mitigating overfitting.
1 code implementation • 6 Apr 2023 • Daniele Martinelli, Clara Lucía Galimberti, Ian R. Manchester, Luca Furieri, Giancarlo Ferrari-Trecate
We validate the properties of NodeRENs, including the possibility of handling irregularly sampled data, in a case study in nonlinear system identification.
1 code implementation • 22 Mar 2022 • Luca Furieri, Clara Lucía Galimberti, Giancarlo Ferrari-Trecate
We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function.
1 code implementation • 16 Dec 2021 • Luca Furieri, Clara Lucía Galimberti, Muhammad Zakwan, Giancarlo Ferrari-Trecate
A main challenge of NN controllers is that they are not dependable during and after training, that is, the closed-loop system may be unstable, and the training may fail due to vanishing and exploding gradients.
3 code implementations • 27 May 2021 • Clara Lucía Galimberti, Luca Furieri, Liang Xu, Giancarlo Ferrari-Trecate
Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation.