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Parallel and vector computation for stochastic optimal control applicationsA general method for parallel and vector numerical solutions of stochastic dynamic programming problems is described for optimal control of general nonlinear, continuous time, multibody dynamical systems, perturbed by Poisson as well as Gaussian random white noise. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random atmospheric fluctuations. The numerical formulation is highly suitable for a vector multiprocessor or vectorizing supercomputer, and results exhibit high processor efficiency and numerical stability. Advanced computing techniques, data structures, and hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations.
Document ID
19900013691
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Hanson, F. B. (Illinois Univ. Chicago, IL, United States)
Date Acquired
September 6, 2013
Publication Date
December 15, 1989
Publication Information
Publication: JPL, Proceedings of the 3rd Annual Conference on Aerospace Computational Control, Volume 1
Subject Category
Computer Systems
Accession Number
90N23007
Funding Number(s)
CONTRACT_GRANT: W-31-109-ENG-38
CONTRACT_GRANT: DE-AC05-84ER-21400
CONTRACT_GRANT: NSF DMS-88-06099
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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