by Michael Athans and Sanjoy K. Mitter. / Final report / Bibliography: p. 17-19. / "March 1979." / Grant AFOSR-77-3281B

by Michael Athans, Sanjoy K. Mitter, Lena Valavani. / Final report. / Bibliography: p. 26-31. / "March 1982." / Air Force Office of Scientific Research Grant AFOSR-77-3281B

by Michael Athans, Sanjoy K. Mitter, Lena Valavani. / Interim report. / Includes bibliographies. / "March 20, 1981." / Air Force Office of Scientific Research Grant AFOSR-77-3281C

Thesis (M.Sc.)--University of Melbourne, Dept. of Otolaryngology, 2000. / Typescript (photocopy). Includes bibliographical references (leaves 99-109).

Thesis (M.S.)--Ohio University, August, 2000. / Title from PDF t.p.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 238-259) Also available in print.

Thesis (M.App.Sc.) - Carleton University, 2005. / Includes bibliographical references (p. 161-165). Also available in electronic format on the Internet.

Thesis (Ph.D)--Chemical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Jay H. Lee; Committee Member: Martha Grover; Committee Member: Matthew J. Realff; Committee Member: Shabbir Ahmed; Committee Member: Stylianos Kavadias. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Yes / 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. / De Montfort University - DMU HEIF’18 project, Natural Science Foundation of Shanxi Province [grant number 201701D221112], National Natural Science Foundation of China [grant numbers 61503271 and 61603136]

Yes / This note presents a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing st