Data-Driven Control: Theory and Applications

被引：12

作者：

Soudbakhsh, Damoon ^{[1
]}

Annaswamy, Anuradha M. ^{[2
]}

Wang, Yan ^{[3
]}

Brunton, Steven L. ^{[4
]}

Gaudio, Joseph ^{[5
]}

Hussain, Heather ^{[5
]}

Vrabie, Draguna ^{[6
]}

Drgona, Jan ^{[6
]}

Filev, Dimitar ^{[3
]}

机构：

[1] Temple Univ, Dept Mech Engn, Philadelphia, PA 19122 USA

[2] MIT, Dept Mech Engn, Boston, MA 19122 USA

[3] Ford Motor Co, Dearborn, MI USA

[4] Univ Washington, Dept Mech Engn, Seattle, WA USA

[5] Boeing Co, Long Beach, CA USA

[6] Pacific Northwest Natl Lab PNNL, Richland, WA USA

来源：

2023 AMERICAN CONTROL CONFERENCE, ACC | 2023年

关键词：

MODEL-PREDICTIVE CONTROL; ADAPTIVE-CONTROL; REDUCTION; IDENTIFICATION; NETWORKS; ADAPTATION; EXCITATION; ALGORITHM; LAWS;

D O I：

10.23919/ACC55779.2023.10156081

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The ushering in of the big-data era, ably supported by exponential advances in computation, has provided new impetus to data-driven control in several engineering sectors. The rapid and deep expansion of this topic has precipitated the need for a showcase of the highlights of data-driven approaches. There has been a rich history of contributions from the control systems community in the area of data-driven control. At the same time, there have been several new concepts and research directions that have also been introduced in recent years. Many of these contributions and concepts have started to transition from theory to practical applications. This paper will provide an overview of the historical contributions and highlight recent concepts and research directions.

引用

页码：1922 / 1939

页数：18

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