Minimum variance (MinVar) method for control performance assessment constitutes one of the most common approaches to the control quality estimation. There are dozens of versions, enriched with numerous reported industrial implementations. MinVar methodology uses the idea of minimum variance, which has been introduced by Kalman. Therefore, it should be remembered that MinVar concept relies on the same assumptions as an idea of the minimum variance control. Among other assumptions, it is essential that the modeled disturbance is an independent random sequence. This article addresses scenarios, when loop noise exhibits non-Gaussian properties and is characterized by outliers having fat-tailed distribution. Sensitivity analysis of minimum variance method against such disturbances is evaluated using commonly used PID SISO control benchmarks. It is shown that MinVar method may be significantly biased in such non-Gaussian situations, which are very frequent in the industrial reality. Reasons of the biased performance are traced and respective solutions are proposed.

中文翻译：

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最小方差控制绩效评估的离群稳健性研究

用于控制性能评估的最小方差 (MinVar) 方法是控制质量评估的最常用方法之一。有几十个版本，丰富了许多报告的工业实现。MinVar 方法使用 Kalman 引入的最小方差的思想。因此，应该记住，MinVar 概念依赖于与最小方差控制思想相同的假设。在其他假设中，建模扰动是一个独立的随机序列是必不可少的。本文讨论了当环路噪声表现出非高斯特性并且以具有肥尾分布的异常值为特征的场景。使用常用的 PID SISO 控制基准来评估针对此类干扰的最小方差方法的灵敏度分析。结果表明，在这种非高斯情况下，MinVar 方法可能存在显着偏差，这种情况在工业现实中非常常见。追溯性能偏差的原因，并提出了相应的解决方案。