211 citations · 378 across the 29 of their papers we have counts for
8 papers · 1 filter
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models
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. Th…
Data-Driven Analytic Differentiation via High Gain Observers and Gaussian Process Priors
Biagio Trimarchi, Lorenzo Gentilini, Fabrizio Schiano +1
The presented paper tackles the problem of modeling an unknown function, and its first derivatives, out of scattered and poor-quality data. The considered setting embraces a…
Adaptive Nonlinear Regulation via Gaussian Process
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. We borrow from the adaptive…
Uniform non-convex optimisation via Extremum Seeking
Nicola Mimmo, Lorenzo Marconi, Giuseppe Notarstefano
The paper deals with a well-known extremum seeking scheme by proving uniformity properties with respect to the amplitudes of the dither signal and of the cost function. Those prope…
Extremum Seeking with Intermittent Measurements: A Lie-brackets Approach
Christophe Labar, Christian Ebenbauer, Lorenzo Marconi
Extremum seeking systems are powerful methods able to steer the input of a (dynamical) cost function towards an optimizer, without any prior knowledge of the cost function. To achi…
Uniform quasi-convex optimisation via Extremum Seeking
Nicola Mimmo, Lorenzo Marconi, Giuseppe Notarstefano
The paper deals with a well-known extremum seeking scheme by proving uniformity properties with respect to the amplitudes of the dither signal and of the cost function. Those prope…