Fault diagnosis of nonlinear systems based on hybrid PSOSA optimization algorithm

被引：1

作者：

Li L.-L. ^{[1
]}

Zhou D.-H. ^{[1
]}

Wang L. ^{[1
]}

机构：

[1] Department of Automation, Tsinghua University

来源：

International Journal of Automation and Computing | 2007年 / 4卷 / 2期

关键词：

Fault diagnosis; Moving horizon estimation; Nonlinear systems; Particle swarm optimization (PSO);

D O I：

10.1007/s11633-007-0183-4

中图分类号：

学科分类号：

摘要：

Fault diagnosis of nonlinear systems is of great importance in theory and practice, and the parameter estimation method is an effective strategy. Based on the framework of moving horizon estimation, fault parameters are identified by a proposed intelligent optimization algorithm called PSOSA, which could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA). Simulations on a three-tank system show the effectiveness of this optimization based fault diagnosis strategy. © 2007 Institute of Engineering Mechanics, China Earthquake Administration.

引用

页码：183 / 188

页数：5

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