1 code implementation • 21 Jan 2022 • Marco Forgione, Aneri Muni, Dario Piga, Marco Gallieri
The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system.
no code implementations • 28 Sep 2020 • Mayank Mittal, Marco Gallieri, Alessio Quaglino, Seyed Sina Mirrazavi Salehian, Jan Koutnik
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides a significant opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account.
no code implementations • 7 Apr 2020 • Giorgio Giannone, Asha Anoosheh, Alessio Quaglino, Pierluca D'Oro, Marco Gallieri, Jonathan Masci
INODE is trained like a standard RNN, it learns to discriminate short event sequences and to perform event-by-event online inference.
no code implementations • 21 Feb 2020 • Mayank Mittal, Marco Gallieri, Alessio Quaglino, Seyed Sina Mirrazavi Salehian, Jan Koutník
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides an opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account.
no code implementations • ICLR 2020 • Sebastian East, Marco Gallieri, Jonathan Masci, Jan Koutnik, Mark Cannon
This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning.
no code implementations • 15 Nov 2019 • Marco Gallieri, Seyed Sina Mirrazavi Salehian, Nihat Engin Toklu, Alessio Quaglino, Jonathan Masci, Jan Koutník, Faustino Gomez
A min-max control framework, based on alternate minimisation and backpropagation through the forward model, is used for the offline computation of the controller and the safe set.
no code implementations • 4 Nov 2019 • Simone Pozzoli, Marco Gallieri, Riccardo Scattolini
The use of recurrent neural networks to represent the dynamics of unstable systems is difficult due to the need to properly initialize their internal states, which in most of the cases do not have any physical meaning, consequent to the non-smoothness of the optimization problem.
no code implementations • ICLR 2020 • Alessio Quaglino, Marco Gallieri, Jonathan Masci, Jan Koutník
This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}) for system identification.
1 code implementation • NeurIPS 2018 • Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino Gomez
This paper introduces Non-Autonomous Input-Output Stable Network(NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system.