Convergence and robustness of iterative learning control for strongly positive systems

Daniel Andres; Madhukar Pandit

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Convergence and robustness of iterative learning control for strongly positive systems

机译：Convergence and robustness of iterative learning control for strongly positive systems

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摘要

In this paper, we consider the convergence and robustness of a general iterative learning control scheme for a class of systems which we term "strongly positive". The analysis is made in the framework of Huhert-space theory. Thus the results are valid for discrete-time as well as continuous-time systems which may be time-variant or time-invariant. For the special case of continuous linear time-invariant systems which are defined over the Hilbert-space of square integrable functions, we will give a characterization of strongly positive systems in the frequency domain.

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《Asian Journal of Control: Affiliated with ACPA, the Asian Control Professors' Association》 |2002年第1期|1-10|共10页
作者
Daniel Andres; Madhukar Pandit;
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作者单位

Department of Applied Physics and Advanced Technologies (FATA), National Autonomous University of Mexico, A.P. 1-1010, Queretaro, Qro. 67000, Mexico;

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原文格式 PDF
正文语种英语
中图分类自动控制理论;
关键词
Learning control; Strongly positive systems; Output dissipative systems;

Convergence and robustness of iterative learning control for strongly positive systems

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