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Title: An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

Abstract

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1411928
Report Number(s):
PNNL-SA-121780
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IFAC PapersOnLine, 50-1(2017):10407-10412
Country of Publication:
United States
Language:
English
Subject:
Stochastic control; Entropy; Tracking error

Citation Formats

Liu, Yunlong, Wang, Aiping, Guo, Lei, and Wang, Hong. An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables. United States: N. p., 2017. Web. doi:10.1016/j.ifacol.2017.08.1720.
Liu, Yunlong, Wang, Aiping, Guo, Lei, & Wang, Hong. An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables. United States. doi:10.1016/j.ifacol.2017.08.1720.
Liu, Yunlong, Wang, Aiping, Guo, Lei, and Wang, Hong. Sun . "An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables". United States. doi:10.1016/j.ifacol.2017.08.1720.
@article{osti_1411928,
title = {An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables},
author = {Liu, Yunlong and Wang, Aiping and Guo, Lei and Wang, Hong},
abstractNote = {This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.},
doi = {10.1016/j.ifacol.2017.08.1720},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jul 09 00:00:00 EDT 2017},
month = {Sun Jul 09 00:00:00 EDT 2017}
}

Conference:
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