Recursive identification algorithm for dynamic systems with output backlash and its convergence

International Journal of Applied Mathematics and Computer Science

Volumn 19, Issue 4, 2009, Pages 631-638

  Dong, Ruili ^a   Tan, Qingyuan ^b   Tan, Yonghong ^a  

^a SHANGHAI NORMAL UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Backlash; Nonlinear system; Pseudo Wiener model; Recursive identification

Indexed keywords

DYNAMIC SYSTEMS; IDENTIFICATION ALGORITHMS; LINEARIZED MODELS; NUMERICAL EXAMPLE; PIECE-WISE; PSEUDO-WIENER MODEL; RECURSIVE IDENTIFICATION; RECURSIVE IDENTIFICATION ALGORITHMS; WIENER MODELS; WIENER SYSTEMS;

ALGORITHMS; DYNAMICAL SYSTEMS; NONLINEAR SYSTEMS;

CONVERGENCE OF NUMERICAL METHODS;

EID: 73949132401     PISSN: 1641876X     EISSN: None     Source Type: Journal    
DOI: 10.2478/v10006-009-0050-2     Document Type: Article

References (18)

1. Boutayeb, M. and Darouach, M. (1995). Recursive identification method for MISO Wiener-Hammerstein model, IEEE Transactions on Automatic Control 40(2): 287-291.
2. Campos, J., Lewis, F. L. and Selmic, R. (2000). Backlash compensation in discrete time nonlinear systems using dynamic inversion by neural networks, International Conference on Automation and Robotics Research, San Francisco, CA, USA, pp. 1289-1295.
3. Celka, P., Bershad, N. J. and Vesin, J. M. (2001). Stochastic gradient identification of polynomial Wiener systems: Analysis and application, IEEE Transactions on Signal Processing 49(2): 301-313.
4. Chandler, H. W., George, S. D., Liddle, J. and Nixon, S. A. (2000). Backlash: A feature of the cyclic deformation of ceramic pastes, Journal of the European Ceramic Society 20(11): 1699-1705.
5. Chen, H. F. (2006). Recursive identification algorithms for Wiener model with discontinuous piecewise-linear linear function, IEEE Transactions on Automatic Control 51(3): 390-400.
6. Fang, C. Z. (2004). System Identification, Qinghua University, Beijing, (in Chinese).
7. Greblicki, W. (1998). Continuous-time Wiener system identification, IEEE Transactions on Automatic Control 43(10): 1488-1493.
8. Greblicki, W. (2001). Recursive identification of Wiener systems, International Journal of Applied Mathematics and Computer Science 11(4): 977-991.
9. Hu, X. L. and Chen, H. F. (2005). Strong consistence of recursive identification for Wiener systems, Automatica 41(11): 1905-1916.
10. Ljung, L. (1977a). Analysis of recursive stochastic algorithms, IEEE Transactions on Automatic Control AC-22(4): 551-575.
11. Ljung, L. (1977b). On positive real transfer functions and the convergence of some recursive schemes, IEEE Transactions on Automatic Control AC-22(4): 539-551.
12. Nordin, M. and Gutman, P.-O. (2002). Controlling mechanical systems with backlash-A survey, Automatica 38(10): 1633-1649.
13. Pearson, R. K. and Pottmann, M. (2000). Gray-box identification of block-oriented nonlinear models, Journal of Process Control 10(4): 301-315.
14. Selmic, R. R. and Lewis, F. L. (2001). Neural net backlash compensation with Hebbian tuning using dynamic inversion, Automatica 37(8): 1269-1277.
15. Toyozawa, Y., Kashiwag, H. and Harada, H. (2004). Identification of Volterra kernels of nonlinear system having backlash type nonlinearity, 30th Annual Conference of the IEEE Industrial Electronics Society, Busan, Korea, pp. 2798- 2802. (Pubitemid 40845515)
16. Voros, J. (1995). Identification of nonlinear dynamic system using extended Hammerstein and Wiener models, Control- Theory Advanced Technology 10(4): 1203-1212.
17. Voros, J. (2001). Parameter identification of Wiener systems with discontinuous nonlinearities, System and Control Letters 44(5): 363-372.
18. Wigren, T. (1997). Circle criteria in recursive identification, IEEE Transactions on Automatic Control 42(7): 975-979.