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Variance minimization and dual control.

Fu, Peilin.

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Variance minimization and dual control.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Variance minimization and dual control./
Author:	Fu, Peilin.
Description:	157 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3504.
Contained By:	Dissertation Abstracts International64-07B.
Subject:	Engineering, System Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3099304

Variance minimization and dual control.
Fu, Peilin.

Variance minimization and dual control. - 157 p.

Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3504.

Thesis (Ph.D.)--Chinese University of Hong Kong (People's Republic of China), 2003.

Except for a few ideal situations, the coupling between optimization and estimation makes an analytical form of optimal dual control unattainable. Previous efforts in dual control have mainly been devoting to developing certain suboptimal solution schemes by bypassing this essential feature of coupling in dual control. Most resulting suboptimal control laws are of a nature of passive learning, since the function of future active probing of the control is purposely deprived in order to achieve an analytical attainability in the solution process.Subjects--Topical Terms:

1018128
Engineering, System Science.

Variance minimization and dual control.
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035 $a (UnM)AAI3099304
035 $a AAI3099304
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100 1 $a Fu, Peilin. $3 1950976
245 1 0 $a Variance minimization and dual control.
300 $a 157 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3504.
500 $a Adviser: Duan Li.
502 $a Thesis (Ph.D.)--Chinese University of Hong Kong (People's Republic of China), 2003.
520 $a Except for a few ideal situations, the coupling between optimization and estimation makes an analytical form of optimal dual control unattainable. Previous efforts in dual control have mainly been devoting to developing certain suboptimal solution schemes by bypassing this essential feature of coupling in dual control. Most resulting suboptimal control laws are of a nature of passive learning, since the function of future active probing of the control is purposely deprived in order to achieve an analytical attainability in the solution process.
520 $a To power a control law with a property of active learning is a key to developing an optimal control law in dual control. A novel variance minimization approach is proposed in this dissertation to enable a finding of an active dual control law for a class of discrete-time LQG control problems with parameter uncertainties. An embedding scheme is proposed to overcome nonseparability in variance minimization. The issue of how to determine the degree of active learning is then addressed.
520 $a For the LQG system with parameter uncertainty only in the observation matrix, an optimal dual control law can be achieved under a solution framework. If the parameter uncertainty exists only in the state matrix, we derive an optimal control policy for the one-dimensional system with perfect observation. For the cases where the parameter uncertainties exist in both state equation and observation equation, an adoption of a variance minimization approach powers the optimal open-loop feedback control law with an active learning property. The best possible closed-loop feedback control is further explored by investigating the future nominal posterior probabilities. The effects of different types of control are illustrated and are compared in several numerical examples.
520 $a Another research issue addressed in this dissertation is variance minimization. Variance minimization problems are widely encountered in real-world application. Due to, however, the associated properties of nonconvexity and nonseparability, such problems are notorious in optimization and optimal control theory. Convexification and separation schemes adopted in this dissertation seem to be promising to overcome the analytical and computational difficulties in variance minimization, which make it possible to go further to study the classical LQG problem under a mean-variance framework. Thus the variance of the performance index in LQG is under control as well as the expected value of it.
590 $a School code: 1307.
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Operations Research. $3 626629
690 $a 0790
690 $a 0544
690 $a 0796
710 2 0 $a Chinese University of Hong Kong (People's Republic of China). $3 1249360
773 0 $t Dissertation Abstracts International $g 64-07B.
790 1 0 $a Li, Duan, $e advisor
790 $a 1307
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3099304