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Found 19 papers, 2 papers with code
Meta results on data-driven control of nonlinear systems
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.
Setpoint control of bilinear systems from noisy data
We consider the problem of designing a controller for an unknown bilinear system using only noisy input-states data points generated by it.
Controller synthesis for input-state data with measurement errors
We consider the problem of designing a state-feedback controller for a linear system, based only on noisy input-state data.
Controller Synthesis from Noisy-Input Noisy-Output Data
We consider the problem of synthesizing a dynamic output-feedback controller for a linear system, using solely input-output data corrupted by measurement noise.
Enforcing contraction via data
We present data-based conditions for enforcing contractivity via feedback control and obtain desired asymptotic properties of the closed-loop system.
Data-driven control of nonlinear systems from input-output data
The design of controllers from data for nonlinear systems is a challenging problem.
Data-driven input-to-state stabilization with respect to measurement errors
We consider noisy input/state data collected from an experiment on a polynomial input-affine nonlinear system.
Learning controllers from data via kernel-based interpolation
We propose a data-driven control design method for nonlinear systems that builds on kernel-based interpolation.
Data-based Transfer Stabilization in Linear Systems
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.
Event-triggered Control From Data
We present a data-based approach to design event-triggered state-feedback controllers for unknown continuous-time linear systems affected by disturbances.
Learning Controllers from Data via Approximate Nonlinearity Cancellation
We introduce a method to deal with the data-driven control design of nonlinear systems.
Data-driven design of safe control for polynomial systems
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.
Learning controllers for performance through LMI regions
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).
Data-driven control via Petersen's lemma
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.
Online learning of data-driven controllers for unknown switched linear systems
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.
Designing Experiments for Data-Driven Control of Nonlinear Systems
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.
On data-driven stabilization of systems with quadratic nonlinearities
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.
Direct data-driven model-reference control with Lyapunov stability guarantees
In this work, we introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state.
Trade-offs in learning controllers from noisy data
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.
