Bias-compensating method for a continuous-time model estimation by using adaptive observer

In this paper, a bias-compensating method for a continuous-time model estimation by using adaptive observer is proposed. It is assumed that the observation noise is a white Gaussian signal while there are no process noises. The proposed method is applicable for the identification in the closed loop environment when the plant has a pole on the imaginary axis. It is shown that the proposed estimate is consistent.