摘要
为了对无刷直流电动机的非线性系统实现快速、精确的故障检测,采用精确的无刷电机非线性系统模型,并应用RBF神经网络,设计了一种非线性状态观测器,通过观测器的估计值与实际输出值之间的残差来判定无刷电机故障与否,并将无刷直流电动机非线性模型在某一工作点附近线性化,采用线性观测器的方法对其进行故障诊断的仿真并与非线性故障诊断方法相比较。结果表明,对于在多工作点工作的无刷直流电机,该方法能获得更精确的故障检测结果。
In order to achieve fast and precise fault detection of brushless direct current electric motor on nonlinear system, this paper adopts an accurate nonlinear model for the motor. Based on the nonlinear model, a nonlinear observer using RBF neural network is designed. The residual between the observer output and real output of the motor is used to detect the faults of the motor. In order to testify that the nonlinear method has more advantages than the linear one, the nonlinear model is linearized at one operation point of the motor and a linear observer is designed. Through comparison of the fault detection result based on linear observer with that of nonlinear observer, we can draw a conclusion that the nonlinear observer approach can effectively detect the faults of nonlinear brushless DC motor that works at several operation points.
出处
《电机与控制学报》
EI
CSCD
北大核心
2006年第1期4-8,共5页
Electric Machines and Control
基金
国家自然基金重点项目(60234010)
