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Exponential Stability of Nonlinear Differential Repetitive Processes with Applications to Iterative Learning Control

机译:非线性差分重复过程的指数稳定性   与迭代学习控制的应用

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摘要

This paper studies exponential stability properties of a class oftwo-dimensional (2D) systems called differential repetitive processes (DRPs).Since a distinguishing feature of DRPs is that the problem domain is bounded inthe "time" direction, the notion of stability to be evaluated does not requirethe nonlinear system defining a DRP to be stable in the typical sense. Inparticular, we study a notion of exponential stability along the discreteiteration dimension of the 2D dynamics, which requires the boundary data forthe differential pass dynamics to converge to zero as the iterations evolve.Our main contribution is to show, under standard regularity assumptions, thatexponential stability of a DRP is equivalent to that of its linearizeddynamics. In turn, exponential stability of this linearization can be readilyverified by a spectral radius condition. The application of this result toPicard iterations and iterative learning control (ILC) is discussed.Theoretical findings are supported by a numerical simulation of an ILCalgorithm.
机译:本文研究了一类称为微分重复过程(DRP)的二维(2D)系统的指数稳定性。由于DRP的一个显着特征是问题域在“时间”方向上有界,因此需要对稳定性进行评估不需要定义DRP的非线性系统在典型意义上是稳定的。尤其是,我们研究了沿二维动力学离散迭代维度的指数稳定性的概念,该概念要求差分遍历动力学的边界数据在迭代发展时收敛为零。我们的主要贡献是在标准正则性假设下证明指数稳定性DRP的线性等效于其线性化动力学。进而,可以通过光谱半径条件容易地验证该线性化的指数稳定性。讨论了该结果在Picard迭代和迭代学习控制(ILC)中的应用。ILC算法的数值模拟为理论研究提供了支持。

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    Altın, Berk; Barton, Kira;

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