Robust identification of continuous systems with dead-time from step responses

In this paper, a simple yet robust method is proposed for identification of linear continuous time-delay processes from step responses. New linear regression equations are derived from the solution and its various-order integrals of the process differential equation. The regression parameters are then estimated without iterations, and explicit relationship between the regression parameters and those in the process are given. Due to use of the process output integrals in the regression equations, the resulting parameter estimation is very robust in the face of large measurement noise in the output. The proposed method is detailed for a second-order plus dead-time model with one zero, which can approximate most practical industrial processes, covering monotonic or oscillatory dynamics of minimum-phase or non-minimum-phase processes. Such a model can be obtained without any iteration. The effectiveness of the identification method has been demonstrated through simulation and real-time implementation.

[1]  Peter E. Wellstead.  Non-parametric methods of system identification , 1981, Autom..

[2]  H. Rake,et al.  Step response and frequency response methods , 1980, Autom..

[3]  Lennart Ljung,et al.  On the estimation of transfer functions , 1985, Autom..

[4]  Gade Pandu Rangaiah,et al.  Estimating Second-Order plus Dead Time Model Parameters , 1994 .

[5]  Riccardo Scattolini,et al.  Recursive estimation of time delay in sampled systems , 1991, Autom..

[6]  Gade Pandu Rangaiah,et al.  Estimating second-order dead time parameters from underdamped process transients , 1996 .

[7]  G. Favier,et al.  Numerical integration approach to on-line identification of continuous-time systems , 1990 .

[8]  H. Unbehauen,et al.  Identification of continuous systems , 1987 .

[9]  Vladimír Strejc,et al.  Trends in identification , 1979, Autom..

[10]  Heinz Unbehauen,et al.  Continuous-time approaches to system identification - A survey , 1990, Autom..

[11]  Mourad Barkat,et al.  Signal detection and estimation , 1991 .

[12]  G. Dumont,et al.  Recursive estimation for system of unknown delay , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[13]  L. Ljung.  On the estimation of transfer functions , 1985 .

[14]  Brett Ninness,et al.  Integral constraints on the accuracy of least-squares estimation , 1996, Autom..

[15]  Wei Xing Zheng,et al.  On a least-squares-based algorithm for identification of stochastic linear systems , 1998, IEEE Trans. Signal Process..

[16]  H. Kurz,et al.  Digital parameter-adaptive control of processes with unknown dead time , 1981, Autom..

[17]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[18]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[19]  P. Fleming,et al.  Method for on-line identification of a first order plus dead-time process model , 1995 .

[20]  Charles George Broyden.  Basic Matrices: An Introduction to Matrix Theory and Practice , 1975 .

[21]  Rik Pintelon,et al.  Real-time integration and differentiation of analog signals by means of digital filtering , 1990 .

[22]  T. Söderström,et al.  Instrumental variable methods for system identification , 1983 .

[23]  Chang Chieh Hang,et al.  Relay auto-tuning in the presence of static load disturbance , 1993, Autom..

[24]  Rik Pintelon,et al.  Identification of transfer functions with time delay and its application to cable fault location , 1990 .