no code implementations • 28 Oct 2022 • Lorenzo Gentilini, Michelangelo Bin, Lorenzo Marconi
This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy.
no code implementations • 24 Jun 2022 • Lorenzo Gentilini, Michelangelo Bin, Lorenzo Marconi
The paper deals with the problem of output regulation of nonlinear systems by presenting a learning-based adaptive internal model-based design strategy.
no code implementations • 24 Nov 2021 • Michelangelo Bin, Lorenzo Marconi
In this paper we propose a new design paradigm, which employing a postprocessing internal model unit, to approach the problem of output regulation for a class of multivariable minimum-phase nonlinear systems possessing a partial normal form.
no code implementations • 16 Dec 2020 • Michelangelo Bin, Thomas Parisini
Moreover, a global and uniform (both in the initial time and in the initial conditions) asymptotic stability result is provided towards a steady state which can be made arbitrarily close to the sought minimum.
no code implementations • 17 Oct 2020 • Michelangelo Bin, Daniele Astolfi, Lorenzo Marconi
Robustness is a basic property of any control system.
no code implementations • 18 Mar 2020 • Stefano Massaroli, Michael Poli, Michelangelo Bin, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network.