A Subspace-Based Method for Continuous-Time Model Identification by Using δ-Operator Model

Abstract

In this paper we derive a subspace-based state-space identification method for continuous-time systems. By using δ-operator we transform the continuous-time system to a discrete-time δ-operator state-space model which converges to the original continuous-time model as the sampling period goes to zero. Then we obtain the estimates of system matrices by applying some well-known subspace identification methods such as MOESP to the discrete-time δ-operator state-space model. We give two numerical examples to show the effectiveness of the proposed method. A beneficial comparison with the other methods such as q-, ω-and λ-operator methods is included.