Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System
复制标题
智能粒子滤波器及其在非线性系统故障检测中的应用
DOI：
10.1109/tie.2015.2399396
复制
发表时间：
2015-06-01
影响因子：
7.7
通讯作者：
Zhu, Xiangping
中科院分区：
文献类型：
--
作者：
Yin, Shen;Zhu, Xiangping
粒子滤波器（PF）为非线性和/或非高斯系统的隐状态估计提供了一种新的技术。然而，一般的PF总是遭受粒子的排斥问题，这会导致误导的状态估计结果。为了科普这个问题，一种改进的粒子滤波器，即，提出了一种智能粒子滤波器（IPF）。它的灵感来自于遗传算法。颗粒多样性的贫乏是一般PF颗粒分散化的主要原因。在IPF中，基于遗传算子的策略被设计为进一步提高粒子多样性。应该指出的是，一般PF是具有指定参数的拟议IPF的特例。两个实验结果表明，IPF减轻粒子的扰动，并提供更准确的状态估计结果相比，一般PF。最后，建议IPF实现实时故障检测的三容系统，结果令人满意。
The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.