Phase IQC for the hierarchical performance analysis of uncertain large scale systems

Khaled Laib, A. Korniienko, G. Scorletti, F. Morel
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引用次数: 5

Abstract

This paper investigates the performance analysis of uncertain large scale systems. Due to their complexity, the usual robustness analysis methods based on e.g: μ or Integral Quadratic Constraints (IQC) cannot be practically applied. In order to address this problem, in [1], we propose to represent a large scale system as an interconnection of sub-systems and to perform a hierarchical analysis by propagating the IQC characterization of each uncertain sub-system through the interconnection. For a given computational time, the conservatism of the analysis dramatically depends on the class of IQC under consideration. In this paper, we propose a new class of IQC which characterizes the phase of uncertain system. An application to the robustness analysis of a PLL network reveals that the use of this class of IQC improves the trade-off between conservatism and computation time.
不确定大系统分层性能分析的IQC阶段
本文研究了不确定大系统的性能分析问题。通常基于μ或积分二次约束的鲁棒性分析方法由于其复杂性而不能实际应用。为了解决这个问题,在[1]中,我们提出将一个大型系统表示为子系统的互连,并通过互连传播每个不确定子系统的IQC特征来执行分层分析。对于给定的计算时间,分析的保守性很大程度上取决于所考虑的IQC的类别。本文提出了一类新的表征不确定系统相位的IQC。应用于锁相环网络的鲁棒性分析表明,这类IQC的使用改善了保守性和计算时间之间的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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