A bias-eliminated least-squares method for continuous-time model identification of closed-loop systems

Schemes for system identification based on closed-loop experiments have att racted considerable interest lately. However, most of the existing approach es have been developed for discrete-time models. In this paper, the problem of continuous-time model identification is considered. A bias correction m ethod without noise modelling associated with the Poisson moment functional s approach is presented for indirect identification of closed-loop systems. To illustrate the performances of the proposed method, the bias-eliminated least-squares algorithm is applied to the parameter estimation of a simula ted system via Monte Carlo simulations.