Robust identification of first-order plus dead-time model from step response

In this paper, a simple yet robust identification method for a linear monotonic process, derived from a step test, is proposed. New linear regression equations are derived, from which the parameters of a first-order plus dead-time model can be obtained directly. No iterations in calculation are needed. The proposed method outperforms the existing estimation methods that use step-test responses. The estimation error is smaller in both the time domain and the frequency domain. Furthermore, the method is robust in the presence of large amounts of measurement noise. The effectiveness of the identification method has been demonstrated through a number of simulation examples and a real-time test.