157 citations · 589 across the 64 of their papers we have counts for
74 papers
Output regulation via input-output data
Andrea Bisoffi, Wenjie Liu, Zhongjie Hu +1
From a multi-input-multi-output (MIMO) discrete-time linear system, we collect input-output data affected by noise in the form of an unknown exosignal and, from these data points (…
Event-triggered control of nonlinear systems from data
Hailong Chen, Claudio De Persis, Andrea Bisoffi +1
In a recent paper [8], we introduced a data-based approach to design event-triggered controllers for linear systems directly from data. Here, we extend the results in [8] to a clas…
Data-driven stabilization of nonlinear systems via descriptor embedding
Mohammad Alsalti, Claudio De Persis, Victor G. Lopez +1
We introduce the notion of descriptor embedding for nonlinear systems and use it for the data-driven design of stabilizing controllers. Specifically, we provide sufficient data-dep…
Data-driven harmonic output regulation of a class of nonlinear systems
Zhongjie Hu, Claudio De Persis, John W. Simpson-Porco +1
The paper deals with the data-based design of state-feedback controllers that solve the output regulation problem for a class of nonlinear systems. Inspired by recent developments…
Data-driven input-to-state stabilization
Hailong Chen, Andrea Bisoffi, Claudio De Persis
For the class of nonlinear input-affine systems with polynomial dynamics, we consider the problem of designing an input-to-state stabilizing controller with respect to typical exog…
A Versatile Framework for Data-Driven Control of Nonlinear Systems
Nima Monshizadeh, Claudio De Persis, Pietro Tesi
This note aims to provide a systematic investigation of direct data-driven control, enriching the existing literature not by adding another isolated result, but rather by offering…
Setpoint control of bilinear systems from noisy data
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. The controller should achieve regulation…
Convex Co-Design of Control Barrier Function and Safe Feedback Controller Under Input Constraints
Han Wang, Kostas Margellos, Antonis Papachristodoulou +1
We study the problem of co-designing control barrier functions (CBF) and linear state feedback controllers for continuous-time linear systems. We achieve this by means of a single…
Controller Synthesis from Noisy-Input Noisy-Output Data
Lidong Li, Andrea Bisoffi, Claudio De Persis +1
We consider the problem of synthesizing a dynamic output-feedback controller for a linear system, using solely input-output data corrupted by measurement noise. To handle input-out…
Controller synthesis for input-state data with measurement errors
Andrea Bisoffi, Lidong Li, Claudio De Persis +1
We consider the problem of designing a state-feedback controller for a linear system, based only on noisy input-state data. We focus on input-state data corrupted by measurement er…
Enforcing contraction via data
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. We focus on unknown nonlinear…
Data-driven control of nonlinear systems from input-output data
Xiaoyan Dai, Claudio De Persis, Nima Monshizadeh +1
The design of controllers from data for nonlinear systems is a challenging problem. In a recent paper, De Persis, Rotulo and Tesi, "Learning controllers from data via approximate n…
Data-driven input-to-state stabilization with respect to measurement errors
Hailong Chen, Andrea Bisoffi, Claudio De Persis
We consider noisy input/state data collected from an experiment on a polynomial input-affine nonlinear system. Motivated by event-triggered control, we provide data-based condition…
Feedback linearization through the lens of data
C. De Persis, D. Gadginmath, F. Pasqualetti +1
Controlling nonlinear systems, especially when data are being used to offset uncertainties in the model, is hard. A natural approach when dealing with the challenges of nonlinear c…
Learning controllers from data via kernel-based interpolation
Zhongjie Hu, Claudio De Persis, Pietro Tesi
We propose a data-driven control design method for nonlinear systems that builds on kernel-based interpolation. Under some assumptions on the system dynamics, kernel-based function…
Data-based Transfer Stabilization in Linear Systems
Lidong Li, Claudio De Persis, Pietro Tesi +1
We present a novel framework for transferring the knowledge from one system (source) to design a stabilizing controller for a second system (target). Our motivation stems from the…
Data-driven stabilizer design and closed-loop analysis of general nonlinear systems via Taylor's expansion
Meichen Guo, Claudio De Persis, Pietro Tesi
For data-driven control of nonlinear systems, the basis functions characterizing the dynamics are usually essential. In existing works, the basis functions are often carefully chos…
Event-triggered Control From Data
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. By an event, we mean stat…
Data-driven design of safe control for polynomial systems
Alessandro Luppi, Andrea Bisoffi, Claudio De Persis +1
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. We consider noisy…
Learning controllers for performance through LMI regions
Andrea Bisoffi, Claudio De Persis, Pietro Tesi
In an open-loop experiment, an input sequence is applied to an unknown linear time-invariant system (in continuous or discrete time) affected also by an unknown-but-bounded disturb…
Data-driven control via Petersen's lemma
Andrea Bisoffi, Claudio De Persis, Pietro Tesi
We address the problem of designing a stabilizing closed-loop control law directly from input and state measurements collected in an open-loop experiment. In the presence of noise…
Data-driven stabilization of nonlinear polynomial systems with noisy data
Meichen Guo, Claudio De Persis, Pietro Tesi
In a recent paper we have shown how to learn controllers for unknown linear systems using finite-sized noisy data by solving linear matrix inequalities. In this note we extend this…
Controller design for robust invariance from noisy data
Andrea Bisoffi, Claudio De Persis, Pietro Tesi
For an unknown linear system, starting from noisy open-loop input-state data collected during a finite-length experiment, we directly design a linear feedback controller that guara…
Willems' Fundamental Lemma for State-space Systems and its Extension to Multiple Datasets
Henk J. van Waarde, Claudio De Persis, M. Kanat Camlibel +1
Willems et al.'s fundamental lemma asserts that all trajectories of a linear system can be obtained from a single given one, assuming that a persistency of excitation condition hol…