Parametric identification of noisy closed-loop linear systems

Abstract

The problem of identification of closed-loop linear systems via the direct approach is studied in a general setting. The process noise acting on the plant, the input measurement noise and the output measurement noise may be coloured and they may be arbitrarily correlated with each other. A parameter estimation method is proposed by using the bias correction principle. The kernel of this method is that the noise covariance vector, which determines the noise-induced bias in the standard least-squares estimator, is obtained in an efficient manner. It is shown that the strength of the proposed direct identification method is that it can derive accurate parameter estimates from noisy closed-loop data irrespective of noise dynamics.