Efficient nonlinear predictive control based on structured neural models

International Journal of Applied Mathematics and Computer Science

Volumn 19, Issue 2, 2009, Pages 233-246

  Ławryńczuk, MacIej ^a  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Linearisation; Model predictive control; Neural networks; Optimisation; Process control

Indexed keywords

AUTO-REGRESSIVE; CLOSED-LOOP CONTROL; COMPUTATIONALLY EFFICIENT; EXTERNAL INPUT; LINEARISATION; NEURAL MODELS; NONCONVEX; NONLINEAR MODEL PREDICTIVE CONTROL; NONLINEAR MPC; NONLINEAR PREDICTIVE CONTROL; OPTIMISATION; OPTIMISATIONS; POLYMERISATION; PREDICTION ACCURACY; PREDICTION ERRORS; QUADRATIC PROGRAMMING PROBLEMS; STRUCTURED MODEL;

CLOSED LOOP CONTROL SYSTEMS; NEURAL NETWORKS; OPTIMIZATION; PREDICTIVE CONTROL SYSTEMS; QUALITY CONTROL;

MODEL PREDICTIVE CONTROL;

EID: 69149111436     PISSN: 1641876X     EISSN: None     Source Type: Journal    
DOI: 10.2478/v10006-009-0019-1     Document Type: Article

References (34)

1. Akesson, B. M. and Toivonen, H. T. (2006). A neural network model predictive controller, Journal of Process Control 16(3): 937-946.
2. Bazaraa, M. S., Sherali, J. and Shetty, K. (1993). Nonlinear Programming: Theory and Algorithms, John Wiley & Sons, New York, NY.
3. Clarke, D.W. and Mohtadi, C. (1989). Properties of generalized predictive control, Automatica 25(6): 859-875.
4. Cutler, R. and Ramaker, B. (1979). Dynamic matrix control-A computer control algorithm, Proceedings of the AIChE National Meeting, Houston, TX, USA.
5. Doyle, F. J., Ogunnaike, B. A. and Pearson, R. K. (1995). Nonlinear model-based control using second-order Volterra models, Automatica 31(5): 697-714.
6. Doyle, F. J., R. K. P. and Ogunnaike, B. A. (2001). Identification and Control of Process Systems Using Volterra Models, Springer, New York, NY.
7. Haykin, S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice Hall, Englewood Cliffs, NJ.
8. Henson, M. A. (1998). Nonlinear model predictive control: Current status and future directions, Computers and Chemical Engineering 23(2): 187-202.
9. Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks
10. are universal approximators, Neural Networks 2(5): 359-366.
11. Hussain, M. A. (1999). Review of the applications of neural networks in chemical process control-Simulation and online implementation, Artificial Intelligence in Engineering 13(1): 55-68.
12. ławry?czuk, M. (2007a). A family of model predictive control algorithms with artificial neural networks, International Journal of Applied Mathematics and Computer Science 17(2): 217-232.
13. ławry?czuk, M. (2007b). Suboptimal nonlinear predictive control with structured neural models, in J. M. de Sá, J. M. Alexandre, W. Duch and D. Mandic (Eds.), The 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal, Springer, Heidelberg, pp. 630-639.
14. ławry?czuk, M. and Tadej, W. (2008). A computationally efficient stable dual-mode type nonlinear predictive control algorithm, Control and Cybernetics 37(1): 99-132.
15. LeCun, Y., Denker, J. and Solla, S. (1989). Optimal brain damage, in
16. Touretzky (Ed.), Advances of NIPS2, Morgan Kaufmann, San Mateo, CA, pp. 598-605.
17. Liu, D., Shah, S. L. and Fisher, D. G. (1999). Multiple prediction models for long range predictive control, Proceedings of the IFAC World Congress, Beijing, China, (on CD-ROM).
18. Liu, G. P., Kadirkamanathan, V. and Billings, S. A. (1998). Predictive control for non-linear systems using neural networks, International Journalof Control 71(6): 1119-1132.
19. Luyben, W. L. (1990). Process Modelling, Simulation and Control for Chemical Engineers, McGraw Hill, New York, NY.
20. Maciejowski, J. M. (2002). Predictive Control with Constraints, Prentice Hall, Harlow.
21. Marlin, T. E. (1995). Process Control, McGraw-Hill, New York, NY.
22. Morari, M. and Lee, J. (1999). Model predictive control: Past, present and future, Computers and Chemical Engineering 23(4): 667-682.
23. Nørgaard, M., Ravn, O., Poulsen, N. K. and Hansen, L. K. (2000). Neural Networks for Modelling and Control of Dynamic Systems, Springer, London.
24. Parisini, T., Sanguineti, M. and Zoppoli, R. (1998). Nonlinear stabilization by receding-horizon neural regulators, International Journal of Control 70(3): 341-362.
25. Piche, S., Sayyar-Rodsari, B., Johnson, D. and Gerules, M. (2000). Nonlinear model predictive control using neural networks, IEEE Control Systems Magazine 20(3): 56-62.
26. Pottmann, M. and Seborg, D. E. (1997). A nonlinear predictive control strategy based on radial basis function models, Computers and Chemical Engineering 21(9): 965-980.
27. Qin, S. J. and Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice 11(7): 733-764.
28. Rossiter, J. A. (2003). Model-Based Predictive Control, CRC Press, Boca Raton, FL.
29. Rossiter, J. A. and Kouvaritakis, B. (2001). Modelling and implicit modelling for predictive control, International Journal of Control 74(11): 1085-1095.
30. Scattolini, R. and Bittanti, S. (1990). On the choice of the horizon in long-range predictive control-Some simple criteria, Automatica 26(5): 915-917.
31. Tatjewski, P. (2007). Advanced Control of Industrial Processes, Structures and Algorithms, Springer, London.
32. Tatjewski, P. and ławry?czuk, M. (2006). Soft computing in model-based predictive control, International Journal of Applied Mathematics and Computer Science 16(1): 101-120.
33. Trajanoski, Z. and Wach, P. (1998). Neural predictive control for insulin delivery using the subcutaneous route, IEEE Transactions on Biomedical Engineering 45(9): 1122-1134.
34. Yu, D. L. and Gomm, J. B. (2003). Implementation of neural network predictive control to a multivariable chemical reactor, Control Engineering Practice 11(11): 1315-1323.