Data-driven distribution tracking for stochastic non-linear systems via PID design

Qichun Zhang, Hong Yue

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

This paper investigates the stochastic distribution tracking problem while the probability density function (PDF) of the stochastic non-linear system output can be controlled to desired distribution. To achieve the control objective, a data-driven approach is proposed in which no information of the system model is required. The output PDF can be estimated by kernel density estimation (KDE) based on the collected system output data. Using the estimated PDF, the probability states can be obtained by sampling operation which can be used to re-characterise the PDF of the system output. Thus, the tracking performance can be achieved by PID control. The parametric selection of the controller has been analysed following the identified PDF dynamic model to assure the convergence of the system output. The effectiveness of the presented algorithm is illustrated by a numerical example.
Original languageEnglish
Title of host publication2019 25th International Conference on Automation and Computing (ICAC)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages16-21
Number of pages6
ISBN (Electronic)9781861376664
ISBN (Print)9781861376657
DOIs
Publication statusPublished - 11 Nov 2019
Event25th IEEE International Conference on Automation and Computing - Lancaster, United Kingdom
Duration: 5 Sep 20197 Sep 2019
http://www.cacsuk.co.uk/index.php/conferences/icac

Conference

Conference25th IEEE International Conference on Automation and Computing
CountryUnited Kingdom
CityLancaster
Period5/09/197/09/19
Internet address

Keywords

  • stochastic distribution control
  • non-Gaussian systems
  • probability density function (PDF)
  • data-driven
  • PID

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