Data-driven Linear Quadratic Regulation via Semidefinite Programming

被引：19
|
作者：
Rotulo, Monica [1 ]
De Persis, Claudio [1 ]
Tesi, Pietro [2 ]
机构：
[1] Univ Groningen, ENTEG, NL-9747 AG Groningen, Netherlands
[2] Univ Florence, DINFO, I-50139 Florence, Italy
来源：
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
关键词：
Data-driven control; Linear quadratic regulation; Semidefinite programming;
D O I：
10.1016/j.ifacol.2020.12.2264
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper studies the finite-horizon linear quadratic regulation problem where the dynamics of the system are assumed to be unknown and the state is accessible. Information on the system is given by a finite set of input-state data, where the input injected in the system is persistently exciting of a sufficiently high order. Using data, the optimal control law is then obtained as the solution of a suitable semidefinite program. The effectiveness of the approach is illustrated via numerical examples. Copyright (C) 2020 The Authors.
引用
收藏
页码：3995 / 4000
页数：6
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