Continuous-Time Model Identification From Filtered Sampled Data: Error Analysis

In this article, an upper bound is established for the estimation error of a standard least squares (LS) algorithm used to identify a continuous-time model from filtered, sampled input-output data. It is found that the error has three constituent components due to the initial conditions, observation noise, and sample period. In particular, the initial condition bias is bounded by O(1/[N Delta t]), which requires sufficiently large [N Delta t] for accurate LS estimation. The theoretical results obtained are confirmed by simulation.