Identification of HR nonlinear systems without prior structural information

IEEE Transactions on Automatic Control

Volumn 52, Issue 3, 2007, Pages 442-453

  Bai, Er Wei ^a   Tempo, Roberto ^b   Liu, Yun ^a,c  

^a IA United States   (United States)

^b POLITECNICO DI TORINO   (Italy)

^c Servo Tech Inc   (United States)

Author keywords

Kernel method; Nonlinear system identification; Parameter estimation; System identification

Indexed keywords

ASYMPTOTIC CONVERGENCE; EXPONENTIAL STABILITY; INFINITE IMPULSE RESPONSE; KERNEL METHODS; NONLINEAR SYSTEM IDENTIFICATION; REAL-WORLD APPLICATION; STRUCTURAL INFORMATION; SYSTEM IDENTIFICATIONS;

COMPUTER SIMULATION; IMPULSE RESPONSE; NONLINEAR SYSTEMS;

PARAMETER ESTIMATION;

EID: 34547134073     PISSN: 00189286     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAC.2007.892385     Document Type: Article

References (29)

1. E. W. Bai, "Frequency domain identification of Hammerstein models," IEEE Trans. Autom. Control, vol. 48, no. 4, pp. 530-542, Apr. 2003.
2. S. A. Billings, S. Chen, S. , and M. J. Kronenberg, "Identification of MIMO nonlinear systems using a forward-regression orthogonal estimator," Int. J. Control, vol. 49, pp. 2157-2189, 1988.
3. S. Chen, X. Hong, C. Harris, and X. Wang, "Identification of nonlinear systems using generalized kernel models," IEEE Trans. Control Syst. Technol., vol. 13, no. 3, pp. 401-411, May 2005.
4. M. Duflo, Random Itemtive Models. Berlin, Germany: SpringerVerlag, 1997.
5. J. Fan and I. Gijbels, Local Polynomial Modeling and Its Applications. New York: Chapman and Hall, 1996.
6. A. Georgiev, "Nonparametric system identification by kernel methods," IEEE Trans. Autom. Control, vol. AC-29, no. 3, pp. 356-358, Apr. 1984.
7. R. Haber and L. Keviczky, Nonlinear System Identification: Input-Output Modeling Approach. Norwell, MA: Kluwer, 2000.
8. R. Haber and H. Unbehauen, "Structure identification of nonlinear dynamic systems-A survey on input/output approaches," Automatica, vol. 26, pp. 651-677, 1990.
9. N. Hilgert, R. Senoussi, and J. Vila, "Nonparametric identification of controlled nonlinear time varying processes," SIAM J. Control Optim., vol. 39, pp. 950-960, 2000.
10. A. Juditsky, H. Hjalmarsson, A. Benveniste, B. Delyon, L. Ljung, J. Sjoberg, and Q. Zhang, "Nonlinear block-box models in system identification: Mathematical foundations," Automatica, vol. 31, pp. 1725-1750, 1995.
11. L. Ljung, System Identification: Theory for the User, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999.
12. M. Milanese and C. Novara, "Set membership identification of nonlinear systems," Automática, vol. 40, pp. 957-975, 2004.
13. E. A. Nadaraya, Nonparametric Estimation of Probability Densities and Regression Curves. Dordrecht, The Netherlands: Kluwer, 1989.
14. K. Narendra and S. Li, "Neural networks in control systems," in Mathematical Perspectives on Neural Networks, P. Smolensky, M. Mozer, and D. Rumelhart, Eds. Mahwah, NJ: Lawrence Erlbaum, 1996, ch. 11.
15. E. Parzen, "An estimation of a probability density function and mode," Ann. Math. Statist., vol. 33, pp. 1065-1076, 1962.
16. B. Portier and A. Oulidi, "Nonparametric estimation and adaptive control of functional autoregressive models," SIAM J. Control Optim., vol. 39, pp. 411-432, 2000.
17. J. Poggi and B. Portier, "Nonlinear adaptive tracking using kernel estimators: Estimation and test for linearity," SIAM J. Control Optim., vol. 39, pp. 707-727, 2000.
18. W. Rugh, Nonlinear System Theory. Baltimore, MD: Johns Hopkins Univ. Press, 1981.
19. I. Scott and B. Mulgrew, "Nonlinear system, identification and prediction using orthonormal functions," IEEE Trans. Signal Process., vol. 45, no. 7, pp. 1842-1853, Jul. 1997.
20. J. Sjoberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P.-Y. Glorennec, H. Hjalmarsson, and A. Juditsky, "Nonlinear black-box modeling in system, identification: A unified overview," Automatica, vol. 31, pp. 1691-1724, 1995.
21. E. Sontag, "The ISS philosophy as a unifying framework for stability-like behavior," in Nonlinear Control in the Year 2000, A. Isidori, F. Lamnabhi-Lagarrigue, and W. Respondek, Eds. Berlin, Germany: Springer-Verlag, 2000, vol. 2, pp. 443-468.
22. T. Soderstrom and P. Stoica, System Identification. New York: Prentice-Hall, 1989.
23. T. Soderstrom, P. Van den Hof, B. Wahlberg, and S. Weiland, Eds., "Special issue on data-based modeling and system, identification," Automatica, vol. 4.1, no. 3, pp. 357-562, 2005.
24. L. Ljung and A. Vicino, "Special issue on identification," IEEE Trans. Autom. Control, vol. 50, no. 10, pp. 1477-1634, Oct. 2005.
25. R. Tempo, G. Calafiore, and F. Dabbene, Randomized Algorithms for Analysis and Control of Uncertain System. London, U.K.: SpringVerlag, 2005.
26. J. Voros, "Parameter identification of discontinuous Hammerstein systems," Automatica, vol. 33, no. 6, pp. 1141-1146, 1997.
27. G. Wahba, Spline Models for Observational Data. Philadelphia, PA: SIAM, 1990.
28. L. L. Xie and L. Guo, "How much uncertainty can be dealt with by feedback," IEEE Trans. Autom. Contml, vol. 45, no. 12, pp. 2203-2217, Dec. 2000.
29. Q. Zhang, "Using wavelet network in nonparametric estimation," IEEE Trans. Neural Netw., vol. 8, no. 2, pp. 227-236, Mar. 1997.