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dc.contributor.authorRen, Mifeng
dc.contributor.authorZhang, Qichun
dc.contributor.authorZhang, Jianhua
dc.date.accessioned2019-03-26T14:27:51Z
dc.date.available2019-03-26T14:27:51Z
dc.date.issued2019-03-16
dc.identifier.citationRen, M., Zhang, Q. and Zhang, J. (2019) An introductory survey of probability density function control. Systems Science & Control Engineering, 7 (1), pp. 158-170en
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/17642
dc.descriptionOpen Access articleen
dc.description.abstractProbability 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.en
dc.language.isoenen
dc.publisherTaylor and Francisen
dc.titleAn introductory survey of probability density function controlen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1080/21642583.2019.1588804
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.cclicenceCC BYen
dc.date.acceptance2019-02-26
dc.researchinstituteInstitute of Engineering Sciences (IES)en
dc.funder.otherthe Natural Science Foundation of Shanxi Province [grant number 201701D221112]en
dc.funder.otherthe National Natural Science Foundation of China [grant numbers 61503271 and 61603136]en
dc.funder.otherHEIF project'18-19, De Montfort Universityen


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