论文标题
通过自举乘法噪声来强大的数据驱动输出反馈控制
Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise
论文作者
论文摘要
我们提出了一种强大的数据驱动的输出反馈控制算法，该算法将固有的有限样本模型估算不确定性将固有的有限样本模型估算到控制设计中。该算法具有三个组件：（1）亚空间标识名义模型估计器； （2）一种量化名义模型估计值的非反应方差的自举重采样方法； （3）一种非规定的鲁棒控制设计方法，其中包括带有乘法噪声的最佳动态输出反馈滤波器和控制器。提出方法的关键优势是系统识别和健壮的控制设计过程均使用随机不确定性表示，因此实际固有的统计估计不确定性直接与稳健控制器设计的不确定性直接保持一致。此外，控制设计方法可容纳高度结构化的不确定性表示，该表示可以比现有方法更有效地捕获不确定性形状。我们通过数值实验表明，在各种样本复杂性和稳定性鲁棒性的衡量标准上，提出的强大数据驱动的输出反馈控制器可以显着优于确定性等效控制器。
We propose a robust data-driven output feedback control algorithm that explicitly incorporates inherent finite-sample model estimate uncertainties into the control design. The algorithm has three components: (1) a subspace identification nominal model estimator; (2) a bootstrap resampling method that quantifies non-asymptotic variance of the nominal model estimate; and (3) a non-conventional robust control design method comprising a coupled optimal dynamic output feedback filter and controller with multiplicative noise. A key advantage of the proposed approach is that the system identification and robust control design procedures both use stochastic uncertainty representations, so that the actual inherent statistical estimation uncertainty directly aligns with the uncertainty the robust controller is being designed against. Moreover, the control design method accommodates a highly structured uncertainty representation that can capture uncertainty shape more effectively than existing approaches. We show through numerical experiments that the proposed robust data-driven output feedback controller can significantly outperform a certainty equivalent controller on various measures of sample complexity and stability robustness.
