Stochastic distribution tracking control for stochastic non-linear systems via probability density function vectorisation
Liu, Yefeng and Zhang, Qichun and Yue, Hong (2021) Stochastic distribution tracking control for stochastic non-linear systems via probability density function vectorisation. Transactions of the Institute of Measurement and Control, 43 (14). pp. 3149-3157. ISSN 0142-3312 (https://doi.org/10.1177/01423312211016929)
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Abstract
This paper presents a new control strategy for stochastic distribution shape tracking regarding non-Gaussian stochastic non-linear systems. The objective can be summarised as adjusting the probability density function (PDF) of the system output to any given desired distribution. In order to achieve this objective, the system output PDF has first been formulated analytically, which is time-variant. Then, the PDF vectorisation has been implemented to simplify the model description. Using the vector-based representation, the system identification and control design have been performed to achieve the PDF tracking. In practice, the PDF evolution is difficult to implement in real-time, thus a data-driven extension has also been discussed in this paper, where the vector-based model can be obtained using kernel density estimation (KDE) with the real-time data. Furthermore, the stability of the presented control design has been analysed, which is validated by a numerical example. As an extension, the multi-output stochastic systems have also been discussed for joint PDF tracking using the proposed algorithm, and the perspectives of advanced controller have been discussed. The main contribution of this paper is to propose: (1) a new sampling-based PDF transformation to reduce the modelling complexity, (2) a data-driven approach for online implementation without model pre-training, and (3) a feasible framework to integrate the existing control methods.
ORCID iDs
Liu, Yefeng, Zhang, Qichun and Yue, Hong ORCID: https://orcid.org/0000-0003-2072-6223;-
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Item type: Article ID code: 76678 Dates: DateEvent31 October 2021Published1 June 2021Published Online19 April 2021Accepted12 October 2020SubmittedSubjects: Science > Physics
Technology > Electrical engineering. Electronics Nuclear engineeringDepartment: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 04 Jun 2021 13:49 Last modified: 11 Nov 2024 13:06 URI: https://strathprints.strath.ac.uk/id/eprint/76678