Computational stochastic dynamic programming on a vector multiprocessor

Author

Hanson, Floyd B.

Author_Institution

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

Volume

36

Issue

4

fYear

1991

fDate

4/1/1991 12:00:00 AM

Firstpage

507

Lastpage

511

Abstract

Numerical methods which have been developed to solve optimal feedback control problems for nonlinear, continuous-time dynamical systems, perturbed by Poisson as well as by Gaussian random white noise, are discussed. Predictor-corrector methods are modified for the nonstandard functional partial differential equation of stochastic dynamic programming to treat nonlinearities attributable to quadratic costs and nonsmoothness attributable to control switching. This numerical formulation is highly suitable for vectorization and parallelization techniques. Advanced computing techniques and hardware are used to help alleviate Bellman´s curse of dimensionality in dynamic programming computations

Keywords

control nonlinearities; dynamic programming; optimal control; partial differential equations; stochastic programming; Gaussian random white noise; Poisson; nonlinear continuous-time dynamical systems; nonlinearities; optimal feedback control; parallelization; partial differential equation; predictor-corrector methods; stochastic dynamic programming; vector multiprocessor; vectorization; Automatic control; Control systems; Differential equations; Dynamic programming; Nonlinear control systems; Nonlinear systems; Shape control; Stochastic processes; Stochastic resonance; Vibration control;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.75113

Filename

75113