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An introductory survey of probability density function control
Ren, M. Zhang, Qichun Zhang, J.
Ren, M.
Zhang, Qichun
Zhang, J.
Publication Date
2019-01
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(c) 2019 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/)
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openAccess
Accepted for publication
2019-02-26
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Abstract
Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.
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Citation
Ren M, Zhang Q and Zhang J (2019) An introductory survey of probability density function control. Systems Science and Control Engineering. 7(1): 158-170.
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