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

被引：2

作者：

Liu, Yunlong ^{[1
]}

Wang, Aiping ^{[2
]}

Guo, Lei ^{[3
]}

Wang, Hong ^{[4
]}

机构：

[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China

[2] Anhui Univ, Inst Comp Sci, Hefei, Anhui, Peoples R China

[3] Beihang Univ, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China

[4] Pacific Northwest Natl Lab, 902 Battelle Blvd, Richland, WA 99352 USA

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Minimum error entropy; information potential; non-Gaussian variables; probability density function; stochastic systems;

D O I：

10.1016/j.ifacol.2017.08.1720

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic systems. 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 is compared with the mean-square-error minimization in the simulation results. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：10407 / 10412

页数：6

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