Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models

Journal of the Franklin Institute

Volumn 351, Issue 12, 2014, Pages 5455-5466

  Vörös, Jozef ^a  

^a SLOVAK UNIVERSITY OF TECHNOLOGY   (Slovakia)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; NONLINEAR DYNAMICAL SYSTEMS;

DECOMPOSITION TECHNIQUE; INPUT SATURATION; INPUT/OUTPUT DATUM; ITERATIVE TECHNIQUE; LINEAR-IN-PARAMETERS; MODEL PARAMETERS; MULTIPLE APPLICATIONS; NON-LINEAR MODEL;

PARAMETER ESTIMATION;

EID: 84910142813     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2014.09.025     Document Type: Article

References (43)

1. J. Chen Several gradient parameter estimation algorithms for dual-rate sampled systems J. Frankl. Inst. 351 1 2014 543 554
2. F. Ding, X. Liu, and G. Liu Identification methods for Hammerstein nonlinear systems Digit. Signal Process. 21 2 2011 215 238
3. G. Dolanc, and S. Strmcnik Identification of nonlinear systems using a piecewise-linear Hammerstein model Syst. Control Lett. 54 2 2005 145 158
4. Z. Hasiewicz, and G. Mzyk Combined parametric-nonparametric identification of Hammerstein systems IEEE Trans. Autom. Control 49 2004 1370 1375
5. P. Hu, F. Ding, and J. Sheng Auxiliary model based least squares parameter estimation algorithm for feedback nonlinear systems using the hierarchical identification principle J. Frankl. Inst. 350 10 2013 3248 3259
6. A. Janczak Neural network approach for identification of Hammerstein systems Int. J. Control 76 2003 1749 1766
7. J. Li, F. Ding, and L. Hua Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems Nonlinear Dyn. 75 1-2 2014 235 245
8. Y. Liu, and E.W. Bai Iterative identification of Hammerstein systems Automatica 43 2007 346 354
9. R. Pupeikis On the identification of Hammerstein systems having saturation-like functions with positive slopes Informatica 17 2006 55 68
10. D. Wang, F. Ding, and Y. Chu Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle Inf. Sci. 222 2013 203 212
11. X. Xu, F. Wang, G. Liu, and F. Qian Identification of Hammerstein systems using key-term separation principle, auxiliary model and improved particle swarm optimisation algorithm IET Signal Process. 7 8 2013 766 773
12. C. Yu, C. Zhang, and L. Xie A new deterministic identification approach to Hammerstein systems IEEE Trans. Signal Process. 62 1 2014 131 140
13. E.W. Bai, and J. Reyland Jr. Towards identification of Wiener systems with the least amount of a priori information: IIR cases Automatica 45 4 2009 956 964
14. H.F. Chen Recursive identification for Wiener model with discontinuous piece-wise linear function IEEE Trans. Autom. Control 51 2006 390 400
15. J. Chen, X. Lu, and R. Ding Gradient-based iterative algorithm for Wiener systems with saturation and dead-zone nonlinearities J. Vib. Control 20 4 2014 634 640
16. J. Chen, Y. Zhang, F. Ding, Gradient based iterative algorithm for wiener systems with piece-wise nonlinearities using analytic parameterization methods, Computers and Applied Chemistry vol. 28 (7), pp. 855-857.
17. F. Giri, Y. Rochdi, and F.Z. Chaoui An analytic geometry approach to Wiener system frequency identification IEEE Trans. Autom. Control 54 4 2009 683 696
18. A. Janczak Instrumental variables approach to identification of a class of MIMO Wiener systems Nonlinear Dyn. 48 2007 275 284
19. K. Kazlauskas, and R. Pupeikis On intelligent extraction of an internal signal in a Wiener system consisting of a linear block followed by hard-nonlinearity Informatica 24 1 2013 35 58
20. M. Pawlak, Z. Hasiewicz, and P. Wachel On nonparametric identification of Wiener systems IEEE Trans. Signal Process. 55 2007 482 492
21. R. Pupeikis On recursive parametric identification of Wiener systems Inf. Technol. Control 40 1 2011 21 28
22. D. Wang, and F. Ding Least squares based and gradient based iterative identification for Wiener nonlinear systems Signal Process. 91 5 2011 1182 1189
23. V. Kalaš, L. Jurišica, M. Žalman, S. Almássy, P. Siviček, A. Varga, and D. Kalaš Nonlinear and Numerical Servosystems 1985 Alfa/SNTL Bratislava, Slovakia (in Slovak)
24. R. Dong, Q. Tan, and Y. Tan Recursive identification algorithm for dynamic systems with output backlash and its convergence Int. J. Appl. Math. Comput. Sci. 19 4 2009 631 638
25. F. Giri, Y. Rochdi, A. Brouri, A. Radouane, and F.Z. Chaoui Frequency identification of nonparametric Wiener systems containing backlash nonlinearities Automatica 49 2013 124 137
26. J. Vörös Identification of cascade systems with backlash Int. J. Control 83 6 2010 1117 1124
27. E.W. Bai A blind approach to the Hammerstein-Wiener model identification Automatica 38 2002 967 979
28. E.W. Bai An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems Automatica 34 1998 333 338
29. P. Crama, and J. Schoukens Hammerstein-Wiener system estimator initialization Automatica 40 2004 1543 1550
30. J. Vörös An iterative method for Hammerstein-Wiener systems parameter identification J. Electric. Eng. 55 11-12 2004 328 331
31. D. Wang, and F. Ding Hierarchical least squares estimation algorithm for Hammerstein-Wiener systems IEEE Signal Process. Lett. 19 12 2012 825 828
32. F. Yu, Z. Mao, and M. Jia Recursive identification for Hammerstein-Wiener systems with dead-zone input nonlinearity J. Process Control 23 8 2013 1108 1115
33. J. Vörös, Compound operator decomposition and its application to Hammerstein and Wiener systems, Block-oriented Nonlinear System Identification, Lecture Notes in Control and Information Sciences, Vol. 404, Springer-Verlag Berlin Heidelberg, 35-51, 2010.
34. J. Vörös Modeling and parameter identification of systems with multisegment piecewise-linear characteristics IEEE Trans. Autom. Control 47 1 2002 184 188
35. J. Vörös Parameter identification of static and dynamic nonlinear systems with saturations J. Electric. Eng. 54 3-4 2003 78 82
36. J. Vörös Parametric identification of systems with general backlash Informatica 23 2 2012 283 298
37. J. Vörös Recursive identification of nonlinear cascade systems with time-varying general input backlash (art. no. 014504) J. Dyn. Syst. Meas. Control-Trans. ASME 135 1 2013 (5 pp.)
38. L. Ljung, and T. Söderström Theory and Practice of Recursive Identification 1983 MIT Press Massachusetts
39. M. Chidambaram Computer Control of Processes 2001 CRC Press New York
40. H.J. Yuan Recursive least squares algorithm for nonlinear systems with piece-wise linearities Appl. Mech. Mater. 475-476 2014 960 963
41. S. Yin, S.X. Ding, A. Haghani, H. Hao, and P. Zhang A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process J. Process Control 22 9 2012 1567 1581
42. S. Yin, H. Luo, and S.X. Ding Real-time implementation of fault-tolerant control systems with performance optimization IEEE Trans. Ind. Electron. 61 5 2014 2402 2411
43. S. Yin, X. Gao, H.R. Karimi, and X. Zhu Study on support vector machine-based fault detection in tennessee eastman process (Article ID 836895) Abstr. Appl. Anal. 2014 2014 (8 pp.)