Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Wang, Aiping; Wang, Hong

doi:10.1002/oca.2755

Title: Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Journal Article · Tue Jul 20 00:00:00 EDT 2021 · Optimal Control Applications and Methods

DOI:https://doi.org/10.1002/oca.2755· OSTI ID:1811400

Wang, Aiping ^[1];

^[2]

Bozhou Univ., Bozhou, Anhui (China). Dept. of Electronic and Information Engineering; Anhui Univ., Hefei (China). Institute of Computer Sciences
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Energy Science and Technology Directorate; Univ. of Manchester (United Kingdom)

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; UK research councils (EPSRC and Leverhulme Trust)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1811400

Journal Information:: Optimal Control Applications and Methods, Vol. 42, Issue 4; ISSN 0143-2087

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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