基于渐消卡尔曼滤波算法的航空发动机参数估计方法

Aero-engine parameters estimation using fading Kalman filter algorithm

针对航空发动自适应模型误差无法完全消除,可能导致参数估计结果严重偏离甚至滤波发散的问题,提出一种带渐消因子的卡尔曼参数估计方法,采用在线调整卡尔曼方程残差的权重、加强现实测量数据在状态估计中作用的策略,保证了发动机性能参数估计的准确性。仿真结果表明,该方法不仅克服了滤波发散现象,具有更优的收敛速度和估计精度,且计算量小,实现简单,便于实际应用。

The deviation of the aero-engine on-board adaptive system model could not be completely eliminated, which may result in serious estimation deviation and filtering divergence. A new Kalman estimation algorithm with fading factor was proposed. Adjusting the weight of innovation eovariance and increasing the effect on realistic measurement data in state estimation, the accuracy of aero-engine parameters estimation was ensured. Compared with the conventional Kalman fihering, the simulation results shows that the method proposed can restrain filtering divergence and obtain the high accuracy of estimation and the short convergence time. The derivation of the new method is simple, the computation amount is little, and the engineering application value is high.