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L-infinity-induced norm analysis of sampled-data systems via piecewise constant and linear approximations

机译:通过分段常数和线性逼近法,对采样数据系统进行L-无穷大诱导范数分析

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This paper deals with the L-1 analysis of linear sampled-data systems, by which we mean the computation of the L-infinity-induced norm of linear sampled-data systems. Two computation methods based on piecewise constant and piecewise linear approximations are provided through fast-lifting, by which the sampling interval [0, h) is divided into M subintervals with an equal width. Even though the central part of the method with the former approximation essentially coincides with a conventional method via fast-sample/fast-hold (FSFH) approximation after all, we show that both methods successfully lead to the upper and lower bounds of the L-infinity-induced norm, whose gap converges to 0 at the rate of 1/M in the former approximation and 1/M-2 in the latter extended approximation. Such achievements are in sharp contrast with an existing result on the former (i.e., FSFH) approximation, which only shows the convergence rate of the error in the resulting estimate of the L-infinity-induced norm, without providing any readily computable upper and lower bounds. A numerical example is given to illustrate the effectiveness of these methods. (C) 2014 Elsevier Ltd. All rights reserved.
机译:本文涉及线性采样数据系统的L-1分析,这是指线性采样数据系统的L无限诱导范数的计算。通过快速提升,提供了基于分段常数和分段线性近似的两种计算方法,通过该方法,将采样间隔[0,h)分为等宽的M个子间隔。即使使用前一种近似方法的方法的中心部分与通过快速采样/快速保持(FSFH)近似的传统方法本质上完全一致,我们仍显示这两种方法均成功导致了L-的上界和下界无穷大诱导范数,其间隙在前一种近似下以1 / M的速率收敛到0,在后一种近似下以1 / M-2的速率收敛到0。这些成就与前一个近似值(即FSFH)的现有结果形成鲜明对比,后者仅显示了L无限诱导范数的最终估计中误差的收敛速度,而没有提供任何易于计算的上限和下限界限。数值例子说明了这些方法的有效性。 (C)2014 Elsevier Ltd.保留所有权利。

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