Filtering approximation using systematic perturbations of a discrete-time stochastic dynamical system [groundwater pollutant remediation]

Author

Kern, Daniel L. ; Hanson, Floyd B.

Author_Institution

Dept. of Math. Stat. & Comput. Sci., Illinois Univ., Chicago, IL, USA

Volume

1

fYear

1999

fDate

1999

Firstpage

445

Abstract

The standard problem of groundwater pollutant remediation by well pumping is modeled as a discrete-time LQG stochastic optimal control problem. The control is approximated by using a variation of differential dynamic programming (DDP) that includes systematic perturbations. Kalman filtering is used to estimate the partially observed state variables in a tractable format. This is a filtering application of the DDP method used by the authors in an earlier perturbation paper

Keywords

Kalman filters; discrete time systems; dynamic programming; filtering theory; groundwater; linear quadratic Gaussian control; stochastic systems; water pollution; Kalman filtering; differential dynamic programming; discrete-time LQG stochastic optimal control problem; discrete-time stochastic dynamical system; filtering approximation; groundwater pollutant remediation; partially observed state variables; systematic perturbations; well pumping; Control systems; Cost function; Dynamic programming; Filtering; Optimal control; Pollution; State estimation; Stochastic processes; Stochastic resonance; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1999. Proceedings of the 1999

Conference_Location

San Diego, CA

ISSN

0743-1619

Print_ISBN

0-7803-4990-3

Type

conf

DOI

10.1109/ACC.1999.782867

Filename

782867