Convergence Analysis of the Auxiliary Model Identification Algorithm for Multivariable Systems
Feng Ding · Control theory & applications · 1997
The convergence of the recursive auxiliary model (RAM)identification algorithm was proved provided that both ratio of the maximum to minimum eigenvalues of the covariance matrix P0-1(t) =∑(s) [i. e. conditional number] and the variance of the observation noise {w(t)} are bounded in reference [1]. In this paper,the convergence rate of RAM algorithm is studied under either unbounded conditional number or unbounded noise variance. The results show that the parameter estimates given by RAM algorithm are consistently convergent and that this algorithm is robust.