Robust Performance Analysis of Linear Parameter-Varying Systems Using Data-Driven Integral Quadratic Constraints

IF 1.9 Q2 AUTOMATION & CONTROL SYSTEMS
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-30 DOI:10.1109/LCSYS.2026.3708566
Emily Burgin;Peter Seiler;Harald Pfifer
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引用次数: 0

Abstract

Data-driven approaches for assessing robustness to uncertainty are gaining popularity as systems become more complex and harder to model accurately. This letter proposes a robustness analysis method using the integral quadratic constraint (IQC) framework. It is developed for linear parameter-varying (LPV) systems using time-domain IQCs. These time-domain IQCs are formulated directly using measured input-output data from the uncertainty. With one optimisation in the form of a semi-definite program (SDP), the optimal performance with respect to measured input-output behaviour of the uncertainty is determined, as well as the corresponding optimal IQC multiplier.
基于数据驱动积分二次约束的线性变参数系统鲁棒性能分析
随着系统变得越来越复杂和难以准确建模,用于评估不确定性鲁棒性的数据驱动方法越来越受欢迎。本文提出了一种使用积分二次约束(IQC)框架的鲁棒性分析方法。利用时域IQCs对线性变参数(LPV)系统进行了研究。这些时域iqc是直接使用测量的不确定度的输入输出数据制定的。通过半确定程序(SDP)形式的优化,确定了相对于不确定性的测量输入输出行为的最佳性能,以及相应的最佳IQC乘数。
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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来源期刊
IEEE Control Systems Letters
IEEE Control Systems Letters Mathematics-Control and Optimization
CiteScore
4.40
自引率
13.30%
发文量
471
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