Identification of coefficients of a low order continuous time transfer function from discrete-time recorded measurements

• Autorzy: Janiszowski K. B.
• Języki publikacji: EN

Abstrakty

EN

The paper deals with a problem of identification of parameters of a continuous time (CT) transfer function for low order linear systems with discrete-time (DT) recorded data. Algorithms for a direct estimation of the CT-coefficients are developed from rules for transformation of a CT-transfer function controlled via a zero-order sampling unit into DT-representation. Two schemes are derived and tested: one based on the Goodwin transformation and the second derived from the modified Tustin transformation. Both approaches have resulted in similar relations, which can be used for direct estimation of the CT-coefficients of the model of an investigated system. The numerical schemes contain some expressions, that are like DT-differences and in effect they can magnify impacts of different disturbances. Therefore the paper presents results of extended testing of both schemes including different type disturbances; measurement noises, slow varying drifts, measurement resolution errors together with changes of the sampling time. A model is used of a third order linear servomechanism system with oscillating and integration actions. A comparison with results determined by the LS-recursive scheme is presented.

Słowa kluczowe

EN

identification; continuous time models; linear systems; discrete time data representation

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2000
• Tom: Vol. 10, no. 1/2
• Strony: 31--46
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wzory

Twórcy

autor

Janiszowski K. B.

• Instytut Automatyki Przemysłowej Politechniki Warszawskiej

Bibliografia

• [1] E. W. Bai: Digital identification of continuous-time system and some related issues. Int. J. Control, 61 (1995), 149-170.
• [2] R. Gessing: Comments on "Modification and the Tustin Approximation" with Concluding Proposition. IEEE Trans., AC 40 (1995), 942-944.
• [3] R. Isermann: Identifikation dynamischer Systeme. Springer Verlag, 1977.
• [4] K. Janiszowski: A Modification of the Tustin approximation. IEEE Trans., AC 38 (1993), 1313-1317.
• [5] K. Janiszowski: Application of Error Covariance Matrices in Identification of Parametric Models. Archives of Control Sciences, 3-4 (1995), 223-251.
• [6] K. Janiszowski and M. Olszewski: State space adaptive control for non-linear systems, in Control Engineering Solutions; a practical approach. P. Albertos (Edit.), IEE Press, (1996), 175-193.
• [7] Z. Kowalczuk: Discrete-time realisation of online continuous-time estimation algorithms. Control and Computers, 23 (1995), 33-37.
• [8] Z. Kowalczu and J. Kozłowski: Bias-free parameter estimation algorithm of continuous-time systems. IEEE Medit. Conf. On Control and Automation, Alghero, (1998), 7.1-7.6.
• [9] L. Ljung: System identification: Theory for the User. Prentice-Hall, Englewood Cliffs, 1987.
• [10] B. M. Ninness and G. C. Goodwin: The relationship between discrete-time and continuous time linear estimation, in Identification of continuous-time systems. N. K. Sinha and G. P. Rao (Eds.), Kluwer Acad. Dortreeht. (1991), 79-122.
• [11] S. Sahara and Z. Y. Zhao: Numerical integration approach to on-line identification of continuous-time systems. Automatica, 26 (1990), 63-74.
• [12] H. Unbehauen and G. P. Rao: Continuous-time approaches to system identification - a survey. Automatica, 26 (1990), 23-35.
• [13] P. Young: Parameter estimation for continuous-time models - a survey. Automatica, 17(1981), 23-39.