Wiener model based nonlinear predictive control

International Journal of Systems Science

Volumn 31, Issue 2, 2000, Pages 189-202

  Gerkšič, Samo ^a   Matko, Drago ^b   Strmčnik, Stanko ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; DISCRETE TIME CONTROL SYSTEMS; LINEAR CONTROL SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; PH EFFECTS; PREDICTIVE CONTROL SYSTEMS; SCHEDULING;

WIENER MODELS;

NONLINEAR CONTROL SYSTEMS;

EID: 0034143143     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/002077200291307     Document Type: Article

References (22)

1. Bazaraa, M S., Sherali, H. D., and Shetty, C. M., 1993, Nonlinear Programming (New York: Wiley).
2. Clarke, D. W., and Scattolini, R., 1991, Constrained receding-hor-izon predictive control. Proceedings of the Institution of Electrical Engineers, Pt-D, 138, 347-354.
3. Fletcher, R., 1987, Practical Methods of Optimization (Chichester, West Sussex: Wiley).
4. Fruzzetti, K. P., Palazoglu, A., and McDonald, K A., 1997, Nonlinear model predictive control using Hammerstein models. Journal of Process Control, 7, 31-41.
5. Garcia, C. E., Prett, D. M., and Morari, M., 1989, Model predictive control: theory and practice - a survey. Automatica, 25, 335-348.
6. Gattu, G., and Zafiriou, E., 1992, Nonlinear quadratic dynamic matrix control with state estimation. Industrial & Engineering Chemistry Research, 31, 1096-1104.
7. Gill, P. E., Murray, W., and Wright, M H., 1981, Practical Optimization (London: Academic Press).
8. Gustafsson, T. K., and Waller, K. V., 1983, Dynamic modelling and reaction invariant control of pH. Chemical Engineering Science, 38, 389-398.
9. Henson, M. A., and Seborg, D. E., 1994, Adaptive nonlinear control of a pH neutralisation process. IEEE Transactions on Control Systems Technology, 2, 169-182
10. Henson, M. A., and Seborg, D. E., 1997, Feedback linearizing control. Nonlinear Process Control, edited by M. A. Henson and D. E. Seborg (Upper Saddle River New Jersey: Prentice Hall), pp. 149-232.
11. Kalafatis, A., Arifin, N., Wang, L., and Cluett, W. R., 1995, A new approach to the identi® cation of pH processes based on the Wiener model. Chemical Engineering Science, 50, 3693-3701.
12. Kavsek-Biassizio, K., SkRJanc, I., and Matko, D., 1997, Fuzzy predictive control of highly nonlinear pH process. Computers and Chemical Engineering, 21, S613-S618.
13. Kinnaert, M., 1989, Adaptive generalized predictive controller for MIMO systems. International Journal of Control, 50, 161-172.
14. Li, W. C., and Biegler L T, 1989, Multistep, Newton-type control strategies for constrained nonlinear processes. Chemical Engineering Research and Design, 67, 562-577.
15. Meadows, E. S., and Rawlings, J. B., 1997, Model predictive control. Nonlinear Process Control, edited by M. A. Henson and D. E. Seborg (Upper Saddle River, New Jersey: Prentice Hall), pp. 233-310.
16. Muske, K. R., and Edgar, T. F., 1997, Nonlinear state estimation. Nonlinear Process Control, edited by M. A. Henson and D. E. Seborg (Upper Saddle River, New Jersey: Prentice Hall), pp. 311-370.
17. Norquay, S. J., Palazoglu A., and Romagnoli, J. A., 1996, Nonlinear model predictive control of pH neutralisation using Wiener models. Proceedings of the 13th IFAC World Congress, Vol. M, San Francisco, California, USA (Oxford: Pergainon), pp. 31-36.
18. te Braake, H., 1997, Neural Control of Biotechnological Processes. PhD thesis, Technische Universiteit Delft.
19. van den Boom, T. J. J., 1996, Model based predictive control, status and perspectives. Proceedings of the IMACS Computational Engineering in Systems Applications' 96 Multiconference, Symposium on Control, Optimization and Supervision, Vol. 1 (Lille: France), pp. 1-12
20. van den Boom, T. J. J., 1997, Robust nonlinear predictive control using feedback linearization and linear matrix inequalities. Proceedings of the American Control Conference, Vol. 5, Albuquerque, New Mexico, USA (Piscataway: NJ), pp. 3068-3072.
21. Westwick, D., and Verhaegen, M, 1996, Identifying MIMO Wiener systems using subspace model identification methods. Signal Processing, 52, 235-258.
22. Wigren, T, 1993, Recursive prediction error identification using the nonlinear Wiener model. Automatica, 29, 1011-1026.