A methodology for the adaptive control of Markov chains under partial state information

Author

Fernández-Gaucherand, Emmanuel ; Arapostathis, Aristotle ; Marcus, Steven I.

Author_Institution

Dept. of Syst. & Ind. Eng., Arizona Univ., Tucson, AZ, USA

fYear

1992

fDate

1992

Firstpage

2750

Abstract

A stochastic adaptive control problem where complete state information is not available to the controller is considered. The system is modeled as a finite stochastic automaton. These models are a slight generalization of the more common partially observable controlled Markov chain models

Keywords

Markov processes; State estimation; adaptive control; finite automata; state estimation; stochastic automata; stochastic systems; Markov chains; finite stochastic automaton; partial state information; stochastic adaptive control; Adaptive control; Automata; Automatic control; Cost function; Optimal control; Process control; State-space methods; Stochastic processes; Stochastic systems; Tin;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-0872-7

Type

conf

DOI

10.1109/CDC.1992.371318

Filename

371318

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2409089