基于约束卡尔曼滤波的航空发动机状态变量模型辨识方法

An Identification Method of Aero-Engine State Variable Model based on Constrained Kalman Filter

为建立高精度的航空发动机状态变量模型,采用约束卡尔曼滤波算法辨识民用涡扇发动机非线性模型以及某型涡轴发动机试车数据状态变量方程矩阵参数。研究表明:基于约束卡尔曼滤波算法能够辨识得到高精度的状态变量模型,相比标准卡尔曼滤波算法,改进的卡尔曼滤波算法可以明显加快模型参数收敛速度、减小稳态误差。

In order to establish a high-precision state variable model(SVM) of aero-engine,the Constrained Kalman filter algorithm was used to identify the matrix parameters of the SVM from the nonlinear model of the civil turbofan engine and the experimental data of a turboshaft engine.It is shown that a high precision state variable model can be identified based on the constraint Kalman filtering method.And the improved Kalman filter can speed up the convergence rate of parameters and reduce the steady state error compared with the standard filtering algorithm.