Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1812-1839 |
| Number of pages | 28 |
| Journal | Optimal Control Applications and Methods |
| Volume | 42 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2021 |
Funding
The writing of the paper is supported in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT‐Battelle, LLC, for the US Department of Energy under contract DE‐AC05‐00OR22725. information Leverhulme Trust, EPSRCThe work presented in this paper reflects the work done by the authors on SDC since 1998, which has been funded by the UK research councils (EPSRC and Leverhulme Trust). The writing of the paper is supported in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. Additionally, Dr. Charles D. Immanuel, formally of Imperial College (UK), has provided some materials on the modeling and control of particle size distribution in chemical engineering systems. These are gratefully acknowledged. This manuscript has been co‐authored by UT‐Battelle, LLC, under contract DE‐AC05‐00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe‐public‐accessplan ).
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | DE‐AC05‐00OR22725 |
| Oak Ridge National Laboratory | |
| Leverhulme Trust |
Keywords
- fault detection and diagnosis
- filtering
- iterative learning mechanisms
- neural networks
- optimization
- probability density functions
- stochastic distribution control systems