Robust identification of continuous-time models with arbitrary time-delay from irregularly sampled data

摘要

This paper presents a new approach to identify continuous-time systems with arbitrary time-delay from irregularly sampled input-output data. It is based on the separable nonlinear least-squares method which combines in a bootstrap manner the iterative optimal instrumental variable method for transfer function model estimation with an adaptive gradient-based technique that searches for the optimal time-delay. Since the objective function may have several local minima with respect to the unknown parameters (especially the time-delay), the initialization requires special attention. Here, a low-pass filtering strategy is used to widen the convergence region around the global minimum. Simulation results are included to show the performance of the proposed method. (C) 2014 Elsevier Ltd. All rights reserved.