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Stochastic lagrangian adaptation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic lagrangian adaptation/ by David Levanony, Peter E. Caines.
作者:	Levanony, David.
其他作者:	Caines, Peter E.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	vi, 77 p. :ill., digital ;24 cm.
內容註:	Introduction -- Problem Statement -- Asymptotic Maximum Likelihood Identification -- Geometric Results -- Lagrangian Adaptation -- Proof of Theorem 5.2 -- Index.
Contained By:	Springer Nature eBook
標題:	Stochastic integral equations. -
電子資源:	https://doi.org/10.1007/978-3-031-73758-9
ISBN:	9783031737589

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

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

705200
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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