Iterative learning control for large scale nonlinear systems with observation noise

被引：34

作者：

Shen, Dong ^{[1
]}

Chen, Han-Fu ^{[1
]}

机构：

[1] Chinese Acad Sci, Key Lab Syst & Control, Inst Syst Sci, AMSS, Beijing 100080, Peoples R China

来源：

AUTOMATICA | 2012年 / 48卷 / 03期

关键词：

Large scale system; Iterative learning control; Nonlinear; Stochastic approximation; Asynchronous distributed control; STOCHASTIC-APPROXIMATION;

D O I：

10.1016/j.automatica.2012.01.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The iterative learning control (ILC) is constructed for the discrete-time large scale systems. Each subsystem is affine nonlinear and its observation equation is with noise. Subsystems are nonlinearly connected via the large state vector of the whole system. The possibility of data missing, and communication delay is taken into account. It is proved that ILC given in the paper with probability one converges to the optimal one minimizing the tracking error. The simulation results are consistent with theoretical analysis. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：577 / 582

页数：6

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