Search Results for author: Marco Gallieri

Found 9 papers, 2 papers with code

On the adaptation of recurrent neural networks for system identification

1 code implementation21 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.

Transfer Learning

Neural Lyapunov Model Predictive Control

no code implementations28 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.

Continuous Control Model Predictive Control

Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples

no code implementations21 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.

Continuous Control Model Predictive Control

Safe Interactive Model-Based Learning

no code implementations15 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.

Safe Exploration

Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systems

no code implementations4 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.

Model-based Reinforcement Learning

SNODE: Spectral Discretization of Neural ODEs for System Identification

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.

NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

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.

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