趋同(经济学)
下降(航空)
梯度下降
数学优化
计算机科学
观察员(物理)
数学
应用数学
人工智能
工程类
经济
物理
经济增长
人工神经网络
量子力学
航空航天工程
作者
Kushal Chakrabarti,Nikhil Chopra
出处
期刊:IEEE Control Systems Letters
日期:2024-01-01
卷期号:8: 1715-1720
被引量:2
标识
DOI:10.1109/lcsys.2024.3416337
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI