Approximation of Feedback Control Systems

Kurt Marti ()
Additional contact information
Kurt Marti: Federal Armed Forces University Munich

Chapter Chapter 8 in Stochastic Optimization Methods, 2024, pp 161-177 from Springer

Abstract: Abstract Optimal feedback controls under stochastic uncertainty can be obtained in general by approximate methods only. In this chapter approximate feedback controls are obtained by Taylor expansion of the state function with respect to the gain matrix or the gain parameters. Since the state function is the solution of the state equation, hence, a first-order system of differential equations, corresponding systems of differential equations for the partial derivatives of the state function with respect to the gain matrix, the gain parameters, resp., can be obtained by partial differentiation of the state equation with respect to the gain matrix, the gain parameters. Corresponding approximate optimal stochastic feedback control problems are then derived.

Date: 2024
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:sprchp:978-3-031-40059-9_8

Ordering information: This item can be ordered from
http://www.springer.com/9783031400599

DOI: 10.1007/978-3-031-40059-9_8

Access Statistics for this chapter

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