Robust nonlinear state estimation for a class of infinite-dimensional systems using reduced-order models
Robust nonlinear state estimation for a class of infinite-dimensional systems using reduced-order models
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DOI：
10.1080/00207179.2019.1645359
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发表时间：
2019-07
影响因子：
2.1
通讯作者：
M. Benosman;J. Borggaard
中科院分区：
文献类型：
--
作者：
M. Benosman;J. Borggaard
针对一类谱无限维非线性系统，提出了一种设计鲁棒低阶观测器的方法。该方法在观测器设计中显式地使用低维子空间。然后，利用鲁棒控制理论中的Lyapunov重构方法，考虑了对有界模型不确定性的鲁棒性。此外，所提出的设计包括数据驱动的学习算法，该算法自动调整观测器增益以优化状态估计的性能。使用流体力学模型-Burgers方程-的数值研究证明了所提出的观测器的有效性。
A methodology for designing robust, low-order observers for a class of spectral infinite-dimensional nonlinear systems is presented. This approach uses the low-dimensional subspace explicitly in the observer design. Then, robustness to bounded model uncertainties is incorporated using the Lyapunov reconstruction method from robust control theory. Furthermore, the proposed design includes a data-driven learning algorithm that auto-tunes the observer gains to optimise the performance of the state estimation. A numerical study using a model from fluid dynamics -Burgers equation- demonstrates the effectiveness of the proposed observer.