论文标题
线性离散时间系统的强大数据驱动的移动范围估计
Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems
论文作者
论文摘要
在本文中，引入了线性时间传播离散时间系统的强大数据驱动移动范围估计（MHE）方案。该方案仅依赖于离线收集的数据，而无需采用任何系统标识步骤。对于在线测量和脱机数据都因非变化和有限噪声而损坏的在线测量和离线数据的设置，我们证明了实用的稳健指数稳定性。通过仿真示例说明了新型鲁棒数据驱动的MHE方案的行为，并将其与基于标准模型的MHE方案进行了比较，其中使用与数据驱动的MHE方案相同的离线数据识别模型。
In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced. The scheme solely relies on offline collected data without employing any system identification step. We prove practical robust exponential stability for the setting where both the online measurements and the offline collected data are corrupted by non-vanishing and bounded noise. The behavior of the novel robust data-driven MHE scheme is illustrated by means of simulation examples and compared to a standard model-based MHE scheme, where the model is identified using the same offline data as for the data-driven MHE scheme.
