The Linear Quadratic Regulator

Tomas Bjork, Mariana Khapko () and Agatha Murgoci ()
Additional contact information
Mariana Khapko: University of Toronto
Agatha Murgoci: Ørsted

Chapter Chapter 3 in Time-Inconsistent Control Theory with Finance Applications, 2021, pp 23-25 from Springer

Abstract: Abstract In order to illustrate the use of dynamic programming and the Bellman equation we now consider a classical engineering problem: The linear quadratic regulator or LQR. The LQR is a well-known design technique in which a process or machine has its settings optimized by minimizing a quadratic cost function. The cost function is often defined as the sum of deviations for key properties (altitude, temperature, etc.). Applications range from underactuated robotics to nanorobotics, general operation of technical systems such as power or climate control systems, physics, statistics, and econometrics.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprfcp:978-3-030-81843-2_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030818432

DOI: 10.1007/978-3-030-81843-2_3

Access Statistics for this chapter

More chapters in Springer Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().