基于SVD卡尔曼滤波器的航空发动机参数估计

Aero-Engine Parameters Estimation Based on SVD-Kalman Filter

航空发动机推力估计所需要的健康参数较多,而安装的传感器数量相对较少。为解决二者之间的矛盾,运用奇异值分解算法,设计了基于该算法的降维卡尔曼滤波器,对用少量传感器发动机的健康参数进行最优估计,进而重构发动机的推力。数字仿真结果验证了其在航空发动机中应用的可能性。

Accurate thrust reconstruction depends on knowledge of health parameters, but there are usually too few sensors to be able to estimate their values. Based on Singular Value Decomposition（SVD）, a new dimension reduction Kalman Filter （KF） is designed. In this new technique, aero-engine health parameters can be optimal estimated by a small amount of sensors, which enable the reconstruction of unmeasured aero-engine outputs, such as thrust. The simulation results indicate that the designed SVD-KF has good performance.