A feedback particle filter-based approach to optimal control with partial observations

Prashant G. Mehta, Sean P. Meyn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In a recent work it is shown that importance sampling can be avoided in the particle filter through an innovation structure inspired by traditional nonlinear filtering combined with Mean-Field Game formalisms. In this paper, the resulting feedback particle filter is used for the purposes of optimal control of a partially observed diffusion process. The feedback particle filter is used to convert the partially observed problem into the fully observed case, and the dynamic programming equations for the same derived. The approach is illustrated by obtaining the HJB equation for the infinite-horizon discounted cost optimal control problem. Two examples are presented. Future applications of the approach to approximate dynamic programming are briefly discussed.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3121-3127
Number of pages7
ISBN (Print)9781467357173
DOIs
StatePublished - Jan 1 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other52nd IEEE Conference on Decision and Control, CDC 2013
Country/TerritoryItaly
CityFlorence
Period12/10/1312/13/13

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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