Recursive IV Identification of Continuous-Time Models With Time Delay From Sampled Data

This brief investigates recursive instrumental variable (IV) identification of time-delayed continuous-time (CT) models from the sampled input-output data. The refined IV method is extended for the first time to identify recursively both the parameters and time delay of a CT transfer function model. It is shown that the proposed method is able to yield consistent parameter estimation. Due to the nonlinear nature of the loss function to be optimized, time-delay estimation suffers from local minima. Thus, the global convergence performance relies on the quality of initial parameters. To increase the chance of converging to the global optimum, several guidelines are suggested to help the choice of starting values. The main results derived in this brief are confirmed by numerical examples and an experimental application.