Bounded-Error Estimator Design with Missing Data Patterns via State Augmentation
Bounded-Error Estimator Design with Missing Data Patterns via State Augmentation
复制标题
通过状态增强进行缺失数据模式的有界误差估计器设计
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Sze Zheng Yong
中科院分区：
文献类型：
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作者：
Syed M. Hassaan;Qiang Shen;Sze Zheng Yong
在本文中，我们提出了一个有界误差估计，实现均衡恢复离散时间时变仿射系统的缺失数据。通过用类Luenberg观测器误差增广系统状态估计，将均衡恢复估计器设计问题表示为半无限优化问题，并利用鲁棒优化工具求解。由于类Luenberg观测器引入的设计自由度，我们可以将增广系统的特征值放置到期望的位置，这导致均衡恢复问题中比文献中现有方法更优的中间水平。此外，作为所提出的均衡恢复估计的扩展，我们考虑在估计器设计中缺失数据，其中使用固定长度的语言来指定允许的缺失数据模式。仿真例子涉及自适应巡航控制系统，以证明所提出的估计器的均衡恢复性能。
In this paper, we present a bounded-error estimator that achieves equalized recovery for discrete-time time-varying affine systems subject to missing data. By augmenting the system state estimate with a Luenberger-like observer error, we formulate the equalized recovery estimator design problem as a semi-infinite optimization problem, and leverage tools from robust optimization to solve it. Due to the design freedom introduced by the Luenberger-like observer, we can place the eigenvalues of the augmented system to desired locations, which results in a more optimal intermediate level in the equalized recovery problem than existing approaches in the literature. Furthermore, as an extension of the proposed equalized recovery estimator, we consider missing data in the estimator design, where a fixed-length language is used to specify the allowable missing data patterns. Simulation examples involving an adaptive cruise control system are given to demonstrate the equalized recovery performance of the proposed estimator.