Robust state estimation and fault detection combining unknown input observer and set-membership approach

Feng Xu, Junbo Tan, Xueqian Wang, V. Puig, Houde Liu, Bin Liang, Bo Yuan
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引用次数: 4

Abstract

This paper aims to propose a new robust state-estimation and fault-detection method by combining the unknown input observer (UIO) and the set-membership estimator (SME). It is known that both the SUIO and the SME can be used to estimate the states of a system. The former aims to obtain a particular value by actively decoupling the effect of unknown inputs, while the latter can obtain state-estimation sets by prediction and correction based on the set theory. Instead of particular state values, the latter can obtain state-estimation sets guaranteeing to contain system states (i.e., robust state estimation). In this paper, we propose to use the framework of the UIO to actively decouple part of unknown inputs and then further employ the set-membership estimation method to estimate state sets and detect faults. The objective of the proposed method is to reduce the conservatism of robust state-estimation sets by using the UIO to remove the contribution of part of unknown inputs to the sizes of state-estimation sets. At the end of this paper, a numerical example is used to illustrate the effectiveness and advantages of the proposed approach.
结合未知输入观测器和集隶属度方法的鲁棒状态估计与故障检测
本文将未知输入观测器(UIO)和集隶属度估计器(SME)相结合,提出了一种新的鲁棒状态估计和故障检测方法。众所周知,SUIO和SME都可以用来估计系统的状态。前者的目标是通过主动解耦未知输入的影响来获得一个特定的值,而后者则是通过基于集合论的预测和修正来获得状态估计集。后者可以获得保证包含系统状态的状态估计集,而不是特定的状态值(即鲁棒状态估计)。在本文中,我们提出利用UIO的框架对部分未知输入进行主动解耦,然后利用集隶属度估计方法估计状态集并检测故障。该方法的目的是通过使用UIO去除部分未知输入对状态估计集大小的贡献来降低鲁棒状态估计集的保守性。最后通过一个算例说明了该方法的有效性和优越性。
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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