论迭代预处理梯度下降(IPG)观测器的收敛性

IF 2.4 Q2 AUTOMATION & CONTROL SYSTEMS
Kushal Chakrabarti;Nikhil Chopra
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引用次数: 0

摘要

这封信探讨了具有采样测量的离散时间非线性动力系统的观测器设计问题。最近提出的迭代预处理梯度下降(IPG)观测器是一种牛顿型观测器,经验表明它比著名的非线性观测器对测量噪声具有更强的鲁棒性,而这正是其他牛顿型观测器所缺乏的特性。然而,IPG 观察器的收敛性并没有得到理论上的保证。这封信针对一类确定性环境下的非线性系统提出了 IPG 观察器的严格收敛性分析,证明了它对实际轨迹的局部线性收敛性。这些假设是现有牛顿型观测器文献中的标准假设,分析进一步证实了 IPG 观测器与牛顿观测器的关系,而这只是之前的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer
This letter considers the observer design problem for discrete-time nonlinear dynamical systems with sampled measurements. The recently proposed Iteratively Preconditioned Gradient-Descent (IPG) observer, a Newton-type observer, has been empirically shown to have improved robustness against measurement noise than the prominent nonlinear observers, a property that other Newton-type observers lack. However, no theoretical guarantees on the convergence of the IPG observer were provided. This letter presents a rigorous convergence analysis of the IPG observer for a class of nonlinear systems in deterministic settings, proving its local linear convergence to the actual trajectory. The assumptions are standard in the existing literature of Newton-type observers, and the analysis further confirms the relation of IPG observer with Newton observer, which was only hypothesized earlier.
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来源期刊
IEEE Control Systems Letters
IEEE Control Systems Letters Mathematics-Control and Optimization
CiteScore
4.40
自引率
13.30%
发文量
471
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