论文标题
输入输出量表 - 增强功能的强大分析
Input-Output-Data-Enhanced Robust Analysis via Lifting
论文作者
论文摘要
从受恒定参数不确定性影响的线性系统的线性分数表示开始，我们演示了如何通过考虑不确定系统的可用输入输出数据来增强标准鲁棒分析测试。我们的方法依赖于提升系统和数据依赖性乘数的构建。它导致了线性矩阵不等式的测试，该测试保证了与观察到的数据相吻合的所有系统的稳定性和性能，如果是肯定的。与许多其他基于数据的方法相反，可以通过利用线性分数表示的能力来纳入有关系统的先前物理或结构知识。
Starting from a linear fractional representation of a linear system affected by constant parametric uncertainties, we demonstrate how to enhance standard robust analysis tests by taking available (noisy) input-output data of the uncertain system into account. Our approach relies on lifting the system and the construction of data-dependent multipliers. It leads to a test in terms of linear matrix inequalities which guarantees stability and performance for all systems compatible with the observed data if it is in the affirmative. In contrast to many other data-based approaches, prior physical or structural knowledge about the system can be incorporated at the outset by exploiting the power of linear fractional representations.
