Application of fast parameter identification in the characteristic model of aerospace craft

Traditionally, the parameters of characteristic model are estimated with recursive least square method. When the parameters changes sharply, the recursive least square method can not track the changes of system parameters effectively. This paper introduced strong tracing filter for the parameters identification of characteristic model. Based on the extended Kalman filter, suboptimal fading factor is introduced into this method which can satisfied the residuals of same time to be minimum and the residuals of different time to be zero simultaneously. The parameter identification method of strong tracking filter is highly capable of tracking varying parameters. This paper applied this method to identify the varying parameters of the characteristic model of the aerospace craft, and the simulation results show that this method allows larger parameter varying range than recursive least square method in the model of aerospace craft.