Variance-reduced model predictive control of Markov jump processes

Peter A. Maginnis, Matthew West, Geir E. Dullerud

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

Abstract

We present an algorithm for variance-reduced Monte Carlo estimates of the expected cost-to-go used in the stochastic model predictive control of Markov jump processes. Specifically, we extend previous work on antithetic stochastic simulation of Markov chains with a finite number of reaction classes to the approximate computation of an expected cost function of a controlled process. In the presence of strict constraints on number of available Monte Carlo samples, we demonstrate significant reduction in the number of Monte Carlo simulations required to achieve a particular cost, including a factor of two reduction in the small resource limit, for a simplified, nonlinear chemical reaction model.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5376-5380
Number of pages5
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Maginnis, P. A., West, M., & Dullerud, G. E. (2016). Variance-reduced model predictive control of Markov jump processes. In 2016 American Control Conference, ACC 2016 (pp. 5376-5380). [7526512] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7526512