107 citations · 299 across the 15 of their papers we have counts for
17 papers
Neural network based control of unknown nonlinear systems via contraction analysis
Hao Yin, Claudio De Persis, Bayu Jayawardhana +1
This paper studies the design of neural network (NN)-based controllers for unknown nonlinear systems, using contraction analysis. A Neural Ordinary Differential Equation (NODE) sys…
Designing Experiments for Data-Driven Control of Nonlinear Systems
Claudio De Persis, Pietro Tesi
In a recent paper we have shown that data collected from linear systems excited by persistently exciting inputs during low-complexity experiments, can be used to design state- and…
On data-driven stabilization of systems with quadratic nonlinearities
Alessandro Luppi, Claudio De Persis, Pietro Tesi
In this paper, we directly design a state feedback controller that stabilizes a class of uncertain nonlinear systems solely based on input-state data collected from a finite-length…
Trade-offs in learning controllers from noisy data
Andrea Bisoffi, Claudio De Persis, Pietro Tesi
In data-driven control, a central question is how to handle noisy data. In this work, we consider the problem of designing a stabilizing controller for an unknown linear system usi…
Direct data-driven model-reference control with Lyapunov stability guarantees
Valentina Breschi, Claudio De Persis, Simone Formentin +1
In this work, we introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is compos…
Data-based stabilization of unknown bilinear systems with guaranteed basin of attraction
Andrea Bisoffi, Claudio De Persis, Pietro Tesi
Motivated by the goal of having a building block in the direct design of data-driven controllers for nonlinear systems, we show how, for an unknown discrete-time bilinear system, t…