适用于飞机舵面损伤情况下的一种在线辨识算法研究

A Better On-Line Identification Algorithm with Impairment of Aircraft Control Surfaces Considered

空中飞行时若舵面出现损伤情况,将会严重影响飞机机动性能,控制律若能在线适应故障,实现对故障状态重新建模,就可极大提高飞机的生存性和可靠性。限制最小二乘算法能够在线辨识出飞机舵面的故障参数变化,可以克服由于噪声和弱信号输入带来辨识不准确的问题。文中用某机型飞行数据对该算法作了仿真验证,说明该算法的有界性、稳定性和收敛性等性能特点是适用于飞机舵面损伤情况下的在线辨识。

Existing on-line identification algorithms are generally least squares algorithms. Due to several causes, these algorithms lead to severe deviation of identified parameters from their true values especially when aircraft control surfaces have been impaired. Our aim is to so improve least squares algorithm as to make its identified parameters fairly reliable. We call our improved least squares algorithm the constrained least squares algorithm. In the full paper we explain in much detail the constrained least squares algorithm; our explanation puts special emphasis on the role of forgotten factor; our explanation also includes detailed analyses of the bounded nature and convergence of constrained least squares algorithm. Finally we give a simulation example that utilized certain flying data. The example shows preliminarily that our constrained least squares algorithm can effectively control the identified parameters to fluctuate in the neighborhood of their true values, thus indicating that our method is bounded, steady and convergent.