Gradient-based step response identification of low-order model for time delay systems

In this paper, a step response identification method is proposed to obtain a low-order model for industrial processes with time delay. Based on a classification of the model pole distribution, the corresponding time domain expressions of the output response to a step change are derived and therefore, a gradient searching algorithm is developed to simultaneously identify the rational model parameters together with the delay parameter. The computation effort can be significantly reduced compared to the existing time domain multiple-integral methods for model fitting. The convergence of the proposed algorithm is analyzed with a strict proof. Two illustrative examples from the literature are shown to demonstrate the effectiveness of the proposed method.