article Closed Access EN 2020-01-01

Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness

Claudio De Persis, Pietro Tesi

Florence Research (University of Florence) (2020)

DOI: 10.1109/tac.2019.2959924

Abstract

In a paper by Willems et al., it was shown that persistently exciting data can be used to represent the input-output behavior of a linear system. Based on this fundamental result, we derive a parametrization of linear feedback systems that paves the way to solve important control problems using data-dependent linear matrix inequalities only. The result is remarkable in that no explicit system's matrices identification is required. The examples of control problems we solve include the state and output feedback stabilization, and the linear quadratic regulation problem. We also discuss robustness to noise-corrupted measurements and show how the approach can be used to stabilize unstable equilibria of nonlinear systems.

Topics

Control Systems and Identification 1.00 Fault Detection and Control Systems 1.00 Advanced Control Systems Optimization 1.00

Field: Engineering · Subfield: Control and Systems Engineering

Keywords

Robustness (evolution),Control theory (sociology),Linear system,Nonlinear system,Parametrization (atmospheric modeling),Robust control,Linear-quadratic-Gaussian control,Quadratic equation,Computer science,Output feedback

Citations by Year

20262025202420232022202120202019
13523218917711799473
53.01
FWCI
100%
Normalized Pctile
43
References
2
Authors

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