no code implementations • 25 Aug 2024 • Zhongjie Hu, Claudio De Persis, Pietro Tesi

The paper deals with the data-based design of controllers that solve the output regulation problem for nonlinear systems.

no code implementations • 16 May 2024 • Nima Monshizadeh, Claudio De Persis, Pietro Tesi

This note aims to provide a systematic understanding of direct data-driven control, enriching the existing literature not by adding another isolated result, but rather by offering a comprehensive, versatile, and unifying framework that sets the stage for future explorations and applications in this domain.

no code implementations • 4 Apr 2024 • Andrea Bisoffi, Dominiek M. Steeman, Claudio De Persis

We consider the problem of designing a controller for an unknown bilinear system using only noisy input-states data points generated by it.

no code implementations • 6 Feb 2024 • Andrea Bisoffi, Lidong Li, Claudio De Persis, Nima Monshizadeh

We consider the problem of designing a state-feedback controller for a linear system, based only on noisy input-state data.

1 code implementation • 4 Feb 2024 • Lidong Li, Andrea Bisoffi, Claudio De Persis, Nima Monshizadeh

We consider the problem of synthesizing a dynamic output-feedback controller for a linear system, using solely input-output data corrupted by measurement noise.

1 code implementation • 15 Jan 2024 • Zhongjie Hu, Claudio De Persis, Pietro Tesi

We present data-based conditions for enforcing contractivity via feedback control and obtain desired asymptotic properties of the closed-loop system.

no code implementations • 17 Sep 2023 • Xiaoyan Dai, Claudio De Persis, Nima Monshizadeh, Pietro Tesi

The design of controllers from data for nonlinear systems is a challenging problem.

no code implementations • 16 Sep 2023 • Hailong Chen, Andrea Bisoffi, Claudio De Persis

We consider noisy input/state data collected from an experiment on a polynomial input-affine nonlinear system.

no code implementations • 19 Apr 2023 • Zhongjie Hu, Claudio De Persis, Pietro Tesi

We propose a data-driven control design method for nonlinear systems that builds on kernel-based interpolation.

no code implementations • 10 Nov 2022 • Lidong Li, Claudio De Persis, Pietro Tesi, Nima Monshizadeh

For each case, by leveraging the data collected from the source system and a priori knowledge on the maximum distance of the two systems, we find a suitable, and relatively small, compact set of systems that contains the actual target system, and then provide a controller that stabilizes the compact set.

no code implementations • 24 Aug 2022 • Claudio De Persis, Romain Postoyan, Pietro Tesi

We present a data-based approach to design event-triggered state-feedback controllers for unknown continuous-time linear systems affected by disturbances.

no code implementations • 25 Jan 2022 • Claudio De Persis, Monica Rotulo, Pietro Tesi

We introduce a method to deal with the data-driven control design of nonlinear systems.

no code implementations • 23 Dec 2021 • Alessandro Luppi, Andrea Bisoffi, Claudio De Persis, Pietro Tesi

We consider the problem of designing an invariant set using only a finite set of input-state data collected from an unknown polynomial system in continuous time.

no code implementations • 20 Oct 2021 • Andrea Bisoffi, Claudio De Persis, Pietro Tesi

For this control design problem, we provide here convex programs to enforce the performance specification from data in the form of linear matrix inequalities (LMI).

no code implementations • 24 Sep 2021 • Andrea Bisoffi, Claudio De Persis, Pietro Tesi

In the cases of data generated by linear and polynomial systems, we conveniently express the uncertainty captured in the set of data-consistent dynamics through a matrix ellipsoid, and we show that a specific form of this matrix ellipsoid makes it possible to apply Petersen's lemma to all of the mentioned cases.

no code implementations • 24 May 2021 • Monica Rotulo, Claudio De Persis, Pietro Tesi

Motivated by the goal of learning controllers for complex systems whose dynamics change over time, we consider the problem of designing control laws for systems that switch among a finite set of unknown discrete-time linear subsystems under unknown switching signals.

no code implementations • 30 Mar 2021 • 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 output-feedback controllers, including optimal Linear Quadratic Regulators (LQR), by solving linear matrix inequalities (LMI) and semidefinite programs.

no code implementations • 29 Mar 2021 • 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 experiment.

no code implementations • 23 Mar 2021 • Valentina Breschi, Claudio De Persis, Simone Formentin, Pietro Tesi

In this work, we introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state.

no code implementations • 15 Mar 2021 • Andrea Bisoffi, Claudio De Persis, Pietro Tesi

Specifically, the feasible set of the latter design problem is always larger, and the set of system matrices consistent with data is always smaller and decreases significantly with the number of data points.

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