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Stochastic lagrangian adaptation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stochastic lagrangian adaptationby David Levanony, Peter E. Caines.
Author:	Levanony, David.
other author:	Caines, Peter E.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	vi, 77 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Stochastic integral equations.
Online resource:	https://doi.org/10.1007/978-3-031-73758-9
ISBN:	9783031737589$q(electronic bk.)

Stochastic lagrangian adaptation
Levanony, David.

Stochastic lagrangian adaptation[electronic resource] /by David Levanony, Peter E. Caines. - Cham :Springer Nature Switzerland :2024. - vi, 77 p. :ill., digital ;24 cm. - SpringerBriefs in mathematics,2191-8201. - SpringerBriefs in mathematics..

Introduction -- Problem Statement -- Asymptotic Maximum Likelihood Identification -- Geometric Results -- Lagrangian Adaptation -- Proof of Theorem 5.2 -- Index.

This book introduces a cutting-edge continuous time stochastic linear quadratic (LQ) adaptive control algorithm for fully observed linear stochastic systems with unknown parameters. The adaptive estimation algorithm is engineered to drive the maximum likelihood estimate into the set of parameters representing the true closed-loop dynamics. By incorporating a performance monitoring feature, this approach ensures that the estimate converges to the true system parameters. Concurrently, it delivers optimal long-term LQ closed-loop performance. This groundbreaking work offers a significant advancement in the field of stochastic control systems.

ISBN: 9783031737589$q(electronic bk.)

Standard No.: 10.1007/978-3-031-73758-9doiSubjects--Topical Terms:

184241
Stochastic integral equations.

LC Class. No.: QA274.27

Dewey Class. No.: 519.22

Stochastic lagrangian adaptation
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505 0 $a Introduction -- Problem Statement -- Asymptotic Maximum Likelihood Identification -- Geometric Results -- Lagrangian Adaptation -- Proof of Theorem 5.2 -- Index.
520 $a This book introduces a cutting-edge continuous time stochastic linear quadratic (LQ) adaptive control algorithm for fully observed linear stochastic systems with unknown parameters. The adaptive estimation algorithm is engineered to drive the maximum likelihood estimate into the set of parameters representing the true closed-loop dynamics. By incorporating a performance monitoring feature, this approach ensures that the estimate converges to the true system parameters. Concurrently, it delivers optimal long-term LQ closed-loop performance. This groundbreaking work offers a significant advancement in the field of stochastic control systems.
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