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Robust Adaptive ELS-QR Algorithm for Linear Discrete Time Stochastic Systems Identification

Authors: Ginalber L. O. Serra

Abstract:

This work proposes a recursive weighted ELS algorithm for system identification by applying numerically robust orthogonal Householder transformations. The properties of the proposed algorithm show it obtains acceptable results in a noisy environment: fast convergence and asymptotically unbiased estimates. Comparative analysis with others robust methods well known from literature are also presented.

Keywords: Stochastic Systems, Robust Identification, Parameter Estimation, Systems Identification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334664

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[1] A. A. Rontogiannis and S. Theodoridis, "New fast QR decomposition least squares adaptive algorithms", IEEE Transactions on Signal Processing, vol. 46, no. 8, pp. 2113-2121, 1998.
[2] A. A. Rontogiannis and S. Theodoridis, "An adaptive LS algorithm based on orthogonal householder transformations", IEEE International Conference on Electronics, Circuits, and Systems, vol.2, pp. 860-863, 1996.
[3] E. Walter and Luc Pronzato, Identification of parametric models from experimental data, Springer, 1994.
[4] G. C. Goodwin and R. L. Payne, Dynamic system identification: experiment design and data analysis, Academic press, 1977.
[5] G.L.O. Serra, A contribution for closed loop identification with adaptive and robust control applications, Master Thesis, Federal University of Maranhão, São Luis-MA, Brasil, 2001.
[6] G.L.O. Serra, "Proposals of methodologies for intelligent identification and control", Doctoral Thesis, State University of Campinas- FEEC/DMCSI, Campinas, São Paulo, Brasil, September 2005.
[7] G.L.O. Serra and C.P. Bottura, "Fuzzy Instrumental Variable Concept and Identification Algorithm", IEEE International Conference on Fuzzy Systems, pp. 1062- 1067, 2005.
[8] G.L.O. Serra and C.P. Bottura, "An IV-QR algorithm for neuro-fuzzy multivariable online identification", IEEE Transactions on Fuzzy Systems, 15 (2), 200-210, 2007.
[9] H. Broman and A. Anderson, "Instrumental variables (IV) and prediction error (PE) like second order recursive algorithms", IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1830-1833, 1996.
[10] H. Dai and K. N. Sinha, "Iterative instrumental variable method for robust identification of systems", IEEE Transactions on Industrial Electronics, vol. 32, no. 5, pp. 480-486, 1995.
[11] H. Hjalmarsson, M. Gevers and F. Bruyne, "For model based control design, closed loop identification gives better performance", Automatica, vol. 32, no. 12, pp. 1659-1673, 1996.
[12] J. E. Bobrow and W. Murray, "An algorithm for RLS identification of parameters that vary quickly with time", IEEE Transactions on Automatic Control, vol. 38, no. 2, pp. 351-354, 1993.
[13] K. J. Astrom and B. Wittermark, Adaptive control, 2nd ed., Addison- Weley, 1995.
[14] L. Ljung, System identification: theory for the user, 2nd ed., Prentice Hall, 1999.
[15] L. A. Aguirre, Introdu├º├úo ├á identifica├º├úo de sistemas: técnicas lineares e n├úo-lineares aplicadas a sistemas reais, 2a ed., UFMG, 2004.
[16] R. G. Jacquot, Modern digital control system, 2nd ed., Marcel Dekker, 1995.
[17] R. J. P. Schrama and P. M. J. V. D. Hof, "Identification and controlclosed loop issues", Automatica, vol. 31, no. 12, pp. 1751-1770, 1995.
[18] R. Skelton and K. Liu, "Closed loop identification and iterative controller design", IEEE Conference on Decision and Control, pp. 482- 487, 1990.
[19] S. S. Wilson and C. L. Camal, "Periodic Instrumental Variable Identification", IEEE, pp. 190-194, 1993.
[20] Y. Zhang, T. T. Lie and C. B. Soh, "Consistent parameter estimation of systems disturbed by correlated noise", IEE Proceedings on Control Theory and Applications, vol. 144, no. 1, pp. 40-44, 1997.