Title :
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
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;
Conference_Titel :
American Control Conference, 1999. Proceedings of the 1999
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4990-3
DOI :
10.1109/ACC.1999.782867
