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

Zhang, Qichun and Yue, Hong; (2019) Data-driven distribution tracking for stochastic non-linear systems via PID design. In: 2019 25th International Conference on Automation and Computing (ICAC). IEEE, GBR, pp. 16-21. ISBN 9781861376664 (

[thumbnail of Zhang-Yue-ICAC2019-Data-driven-distribution-tracking-for-stochastic-non-linear-systems]
Text. Filename: Zhang_Yue_ICAC2019_Data_driven_distribution_tracking_for_stochastic_non_linear_systems.pdf
Accepted Author Manuscript

Download (874kB)| Preview


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.


Zhang, Qichun and Yue, Hong ORCID logoORCID:;