Neural controllers for networked control systems based on minimum tracking error entropy

被引：4
作者：
Zhang, J. H. [1 ]
机构：
[1] N China Elect Power Univ, Dept Automat, Beijing 102206, Peoples R China
来源：
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING | 2008年 / 222卷 / I7期
基金：
北京市自然科学基金; 中国国家自然科学基金;
关键词：
networked control system; entropy; stochastic control; time delay; neural networks;
D O I：
10.1243/09596518JSCE576
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a novel proportional-integral-derivative (PID) feedback control method for networked control systems (NCSs) subject to random delays is presented via minimizing tracking error entropy, which is estimated by Parzen windows and quadratic Gaussian kernels. The PID controller is implemented by backpropagation (BP) neural networks. Specifically, the performance index implies the idea of the minimum entropy control of the closed-loop tracking error. The convergence in the mean square sense has been analysed for closed-loop NCSs. Simulation results are provided to show the effectiveness of the proposed approach.
引用
收藏
页码：671 / 679
页数：9
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