Iterative Learning Control for Discrete-time Stochastic Systems with Quantized Information

被引：0
|
作者：
Shen, Dong [1 ]
Xu, Yun [1 ]
机构：
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
来源：
IEEE-CAA JOURNAL OF AUTOMATICA SINICA | 2016年 / 3卷 / 01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：
Iterative learning control (ILC); quantized information; almost sure convergence; stochastic approximation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An iterative learning control (ILC) algorithm using quantized error information is given in this paper for both linear and nonlinear discrete-time systems with stochastic noises. A logarithmic quantizer is used to guarantee an adaptive improvement in tracking performance. A decreasing learning gain is introduced into the algorithm to suppress the effects of stochastic noises and quantization errors. The input sequence is proved to converge strictly to the optimal input under the given index. Illustrative simulations are given to verify the theoretical analysis.
引用
收藏
页码：59 / 67
页数：9
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