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

• Published in: Optimal control applications & methods Vol. 42; no. 6; pp. 1812 - 1839
• Main Authors: Wang, Aiping, Wang, Hong
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2021; Wiley Subscription Services, Inc
• Subjects: Complex systems; Control systems design; Control tasks; Control theory; Data mining; Decision analysis; Economics; fault detection and diagnosis; filtering; iterative learning mechanisms; neural networks; Optimal control; Optimization; Parameters; Probability density functions; Probability theory; Random variables; Shape control; Signal processing; stochastic distribution control systems; Stochastic processes; Stochastic systems; System theory; Systems analysis; Systems theory; Uncertainty
• ISSN: 0143-2087; 1099-1514
• DOI: 10.1002/oca.2755

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.