A novel recursive MOESP subspace identification algorithm based on forgetting factor
一种新的基于遗忘因子的递推子空间辨识算法

A new recursive subspace identification algorithm is proposed for the recursive estimation of state space model of linear time-varying systems. A forgetting factor is introduced in the Hankel matrices of the input-output data to increase the convergent rate and improve the performance in tracking the time-varying information. In solving the singular value decomposition (SVD) problem, a gradient-type subspace tracking method is employed to update the state-space subspace based on forgetting factor, realizing the unbiased estimation of the extended observability matrix and improving the robustness to the uncertainty in initial values. The proposed method is simple and highly accurate in numerical computation. The convergence of the proposed method is also proved theoretically. Finally, the efficiency of this method is illustrated with a simulation example.