Aerodynamic parameter identification for hypersonic vehicles considering input measurement errors

Aerodynamic parameter identification is one of crucial technologies in the design and control of hypersonic vehicles. In practice, equation error methods (EEM) is used to identify aerodynamic parameters based on input and output measurement data. However, because most of the existing EEMs only consider the output measurement errors regardless of the input measurement errors, the identified aerodynamic parameters always have large deviations. To address the problem of large deviations of aerodynamic parameters where the polynomial regression model is adopted and input measurement errors exist,, the reasons of those large deviations are analyzed in this paper. Then an adjusted least square (ALS) method considering the input measurement errors is introduced to estimate the aerodynamic parameters of hypersonic vehicles. The corresponding identification algorithm is also presented in detail. The proposed method is illustrated on the longitudinal model of hypersonic vehicles, and the results show that the identified aerodynamic parameters obtained by ALS are more accurate than those values estimated by ordinary least square (OLS) method, thus the effectiveness of the method is verified.