Nonparametric instrumental variables for identification of block-oriented systems

• Autorzy: Mzyk G.
• Języki publikacji: EN

Abstrakty

EN

A combined, parametric-nonparametric identification algorithm for a special case of NARMAX systems is proposed. The parameters of individual blocks are aggregated in one matrix (including mixed products of parameters). The matrix is estimated by an instrumental variables technique with the instruments generated by a nonparametric kernel method. Finally, the result is decomposed to obtain parameters of the system elements. The consistency of the proposed estimate is proved and the rate of convergence is analyzed. Also, the form of optimal instrumental variables is established and the method of their approximate generation is proposed. The idea of nonparametric generation of instrumental variables guarantees that the I.V. estimate is well defined, improves the behaviour of the least-squares method and allows reducing the estimation error. The method is simple in implementation and robust to the correlated noise.

Słowa kluczowe

EN

system identification; instrumental variables; NARMAX system; Hammerstein system; Wiener system; Lur'e system; nonparametric method

PL

identyfikacja systemu; zmienna instrumentalna; system Hammersteina; system Wienera; metoda nieparametryczna

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2013
• Tom: Vol. 23, no. 3
• Strony: 521--537
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

Mzyk G.

• Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• [1] Bai, E. (1998). An optimal two-stage identification algorithm for Hammerstein–Wiener nonlinear systems, Automatica 34(3): 333–338.
• [2] Chen, S. and Billings, S. (1989). Representations of non-linear systems: The NARMAX model, International Journal of Control 49(3): 1013–1032.
• [3] Chow, Y. and Teicher, H. (2003). Probability Theory: Independence, Interchangeability, Martingales, Springer-Verlag, New York, NY.
• [4] Findeisen, W., Bailey, F., Brdyś, M., Malinowski, K., Tatjewski, P. and Woźniak, A. (1980). Control and Coordination in Hierarchical Systems, J. Wiley, Chichester/New York, NY.
• [5] Finigan, B. and Rowe, I. (1974). Strongly consistent parameter estimation by the introduction of strong instrumental variables, IEEE Transactions on Automatic Control 19(6): 825–830.
• [6] Giri, F. and Bai, E.W. (2010). Block-Oriented Nonlinear System Identification, Lecture Notes in Control and Information Sciences, Vol. 404, Springer, Berlin.
• [7] Greblicki, W. and Pawlak, M. (2008). Nonparametric System Identification, Cambridge University Press, New York, NY.
• [8] Haber, R. and Keviczky, L. (1999). Nonlinear System Identification: Input-Output Modeling Approach, Kluwer Academic Publishers, Dordrecht.
• [9] Hannan, E. and Deistler, M. (1988). The Statistical Theory of Linear Systems, John Wiley and Sons, New York, NY.
• [10] Hansen, L. and Singleton, K. (1982). Generalized instrumental variables estimation of nonlinear rational expectations models, Econometrica: Journal of the Econometric Society 50(5): 1269–1286.
• [11] Hasiewicz, Z. (1989). Applicability of least-squares to the parameter estimation of large-scale no-memory linear composite systems, International Journal of Systems Science 20(12): 2427–2449.
• [12] Hasiewicz, Z. and Mzyk, G. (2009). Hammerstein system identification by non-parametric instrumental variables, International Journal of Control 82(3): 440–455.
• [13] Hill, D. and Chong, C. (1989). Lyapunov functions of Lur’e–Postnikov form for structure preserving models of power systems, Automatica 25(3): 453–460.
• [14] Hill, D. and Mareels, I. (1990). Stability theory for differential/algebraic systems with application to power systems, IEEE Transactions on Circuits and Systems 37(11): 1416–1423.
• [15] Kincaid, D. and Cheney, E. (2002). Numerical Analysis: Mathematics of Scientific Computing, Vol. 2, American Mathematical Society, Pacific Grove, CA.
• [16] Kowalczuk, Z. and Kozłowski, J. (2000). Continuous-time approaches to identification of continuous-time systems, Automatica 36(8): 1229–1236.
• [17] Kudrewicz, J. (1976). Functional Analysis for Control and Electronics Engineers, PWN, Warsaw, (in Polish).
• [18] Lu, J. and Hill, D. (2007). Impulsive synchronization of chaotic Lur’e systems by linear static measurement feedback: An LMI approach, IEEE Transactions on Circuits and Systems II: Express Briefs 54(8): 710–714.
• [19] Mzyk, G. (2007). Generalized kernel regression estimate for the identification of Hammerstein systems, International Journal of Applied Mathematics and Computer Science 17(2): 189–197, DOI: 10.2478/v10006-007-0018-z.
• [20] Mzyk, G. (2009). Nonlinearity recovering in Hammerstein system from short measurement sequence, IEEE Signal Processing Letters 16(9): 762–765.
• [21] Mzyk, G. (2013). Instrumental variables for nonlinearity recovering in block-oriented systems driven by correlated signal, International Journal of Systems Science, DOI: 10.1080/00207721.2013.775682.
• [22] Rao, C. (1973). Linear Statistical Inference and Its Applications, Wiley, New York, NY.
• [23] Sagara, S. and Zhao, Z.-Y. (1990). Numerical integration approach to on-line identification of continuous-time systems, Automatica 26(1): 63–74.
• [24] Sastry, S. (1999). Nonlinear Systems: Analysis, Stability, and Control, Interdisciplinary Applied Mathematics, Vol. 10, Springer, New York, NY.
• [25] Söderström, T. and Stoica, P. (1983). Instrumental Variable Methods for System Identification, Vol. 161, Springer-Verlag, Berlin.
• [26] Söderström, T. and Stoica, P. (1989). System Identification, Prentice Hall, Englewood Cliffs, NJ.
• [27] Söderström, T. and Stoica, P. (2002). Instrumental variable methods for system identification, Circuits, Systems, and Signal Processing 21(1): 1–9.
• [28] Stoica, P. and Söderström, T. (1982). Instrumental-variable methods for identification of Hammerstein systems, International Journal of Control 35(3): 459–476.
• [29] Suykens, J., Yang, T. and Chua, L. (1998). Impulsive synchronization of chaotic Lur’e systems by measurement feedback, International Journal of Bifurcation and Chaos 8(06): 1371–1381.
• [30] Ward, R. (1977). Notes on the instrumental variable method, IEEE Transactions on Automatic Control 22(3): 482–484.
• [31] Wong, K. and Polak, E. (1967). Identification of linear discrete time systems using the instrumental variable method, IEEE Transactions on Automatic Control 12(6): 707–718.
• [32] Zhang, Y., Bai, E., Libra, R., Rowden, R. and Liu, H. (1996). Simulation of spring discharge from a limestone aquifer in Iowa, USA, Hydrogeology Journal 4(4): 41–54.
• [33] Zhao, Z.-Y., Sagara, S. and Wada, K. (1991). Bias-compensating least squares method for identification of continuous-time systems from sampled data, International Journal of Control 53(2): 445–461.