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

被引：514

作者：

De Persis, Claudio ^{[1
,2
]}

Tesi, Pietro ^{[3
]}

机构：

[1] Univ Groningen, ENTEG, NL-9747 AG Groningen, Netherlands

[2] Univ Groningen, JC Willems Ctr Syst & Control, NL-9747 AG Groningen, Netherlands

[3] Univ Florence, DINFO, I-50139 Florence, Italy

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2020年 / 65卷 / 03期

关键词：

Control design; data-driven control; learning systems; linear matrix inequalities; nonlinear control systems; robust control; DESIGN;

D O I：

10.1109/TAC.2019.2959924

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：909 / 924

页数：16