Exact Identification of Continuous-Time Systems from Sampled Data

Abstract

Both direct and indirect methods exist for continuous-time system identification. A direct method estimates continuous-time input and output signals from their samples and then use them to obtain a continuous-time model, whereas an indirect method estimates a discrete-time model first. Both methods rely on fast sampling to ensure good accuracy. In this paper, we propose a more direct method where a continuous-time model is directly fitted to the available samples. This method produces an exact model asymptotically, modulo some aliasing ambiguity, even when the sampling rate is relatively low.