Characteristic parameters identification of characteristic models of linear time invariant systems

Characteristic model is a widely applied control model in the aerospace systems. The key idea of characteristic model is to approximate the high order complex systems by a linear slowly time varying lower order model. For the linear time invariant(LTI) systems, this paper shows that the characteristic parameters of characteristic model are condensed by the system information of high order LTI form, therefore some tracking algorithms, which are used to deal the slowly time varying parameters that are unrelated with system states, aren't suitable in this case. This paper establishes the connection between the characteristic parameters of characteristic model for LTI systems and subspace method, and presents a composite identification algorithm to estimate these parameters. Furthermore, it is proved that the estimated values approaches to the true values of the characteristic parameters. In simulation example, a single input single output(SISO) 6 order model is considered, and our proposed method is compared with the projecting forgetting factor recursive least square(FFRLS) algorithm. The simulation results show that our method has more advantages than FFRLS.