Neural networks in model predictive control

Studies in Computational Intelligence

Volumn 252, Issue , 2009, Pages 31-63

  Ławryńczuk, MacIej ^a  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Linearisation; Model identification; Model Predictive Control; Neural networks; Optimisation; Process control; Quadratic programming

Indexed keywords

EID: 74049087011     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-04170-9_2     Document Type: Article

References (52)

1. Al-Duwaish, H., Karim, M.N., Chandrasekar, V.: Use of multilayer feedforward neural networks in identification and control of Wiener model. Proceedings IEE, Part D, Control Theory and Applications 143, 225-258 (1996)
2. Alexandridis, A., Sarimveis, H.: Nonlinear adaptive model predictive control based on self-correcting neural network models. AIChE Journal 51, 3495-3506 (2005)
3. Al Seyab, R.K., Cao, Y.: Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. Journal of Process Control 18, 568-581 (2008)
4. ?Akesson, B.M., Toivonen, H.T.: A neural network model predictive controller. Journal of Process Control 16, 937-946 (2006)
5. Arto, V., Hannu, P., Halme, A.: Modeling of chromato-graphic separation process with Wiener-MLP representation. Journal of Process Control 78, 443-458 (2001)
6. Bazaraa, M.S., Sherali, J., Shetty, K.: Nonlinear programming: theory and algorithms. Prentice-Hall, Englewood Cliffs (1999)
7. Bomberger, J.D., Seborg, D.E.: Determination of model order for NARX models directly from input-output data. Journal of Process Control 8, 459-468 (1998)
8. Cavagnari, L., Magni, L., Scattolini, R.: Neural network implementation of nonlinear receding-horizon control. Neural Computing and Applications 8, 86-92 (1999)
9. Doyle, F.J., Ogunnaike, B.A., Pearson, R.K.: Nonlinear model-based control using second-order Volterra models. Automatica 31, 697-714 (1995)
10. El Ghoumari, M.Y., Tantau, H.J.: Non-linear constrained MPC: real-time implementation of greenhouse air temperature control. Computers and Electronics in Agriculture 49, 345-356 (2005)
11. Haykin, S.: Neural networks - a comprehensive foundation. Prentice-Hall, Englewood Cliffs (1999)
12. Henson, M.A.: Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering 23, 187-202 (1998)
13. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366 (1989)
14. Hussain, M.A.: Review of the applications of neural networks in chemical process control - simulation and online implmementation. Artificial Intelligence in Engineering 13, 55-68 (1999)
15. Janczak, A.: Identification of nonlinear systems using neural networks and polynomial models. In: A block-oriented approach. LNCIS, vol.310. Springer, Berlin (2005)
16. Lazar, M., Pastravanu, O.: A neural predictive controller for non-linear systems. Mathematics and Computers in Simulation 60, 315-324 (2002)
17. LeCun, Y., Denker, J., Solla, S.: Optimal brain damage. In: Touretzky, D. (ed.) Advances of NIPS2, pp. 598-605. Morgan Kaufmann, San Mateo (1990)
18. Liu, G.P., Kadirkamanathan, V., Billings, S.A.: Predictive control for non-linear systems using neural networks. International Journal of Control 71, 1119-1132 (1998) (Pubitemid 128552999)
19. Luyben, W.L.: Process modelling, simulation and control for chemical engineers. McGraw Hill, New York (1990)
20. Lawrýnczuk, M.: Efficient nonlinear predictive control based on structured neural models. International Journal of Applied Mathematics and Computer Science 19 (in press, 2009)
21. Lawrýnczuk, M.: Modelling and nonlinear predictive control of a yeast fermentation biochemical reactor using neural networks. Chemical Engineering Journal 145, 290-307 (2008)
22. Lawrýnczuk, M., Tadej, W.: A computationally efficient stable dual-mode type nonlinear predictive control algorithm. Control and Cybernetics 145, 99-132 (2008)
23. Lawrýnczuk, M.: Suboptimal Nonlinear Predictive Control Based on NeuralWiener Models. In: Dochev, D., Pistore, M., Traverso, P. (eds.) AIMSA 2008. LNCS (LNAI), vol.5253, pp. 410-414. Springer, Heidelberg (2008)
24. Lawrýnczuk, M.: Suboptimal nonlinear predictive control with neural multimodels. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J. (eds.) Computational intelligence: methods and applications, pp. 45-56. Exit, Warsaw (2008)
25. Lawrýnczuk, M.: Suboptimal nonlinear predictive control with MIMO neural Hammerstein models. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS (LNAI), vol.5027, pp. 225-234. Springer, Heidelberg (2008)
26. Lawrýnczuk, M.: A family of model predictive control algorithms with artificial neural networks. International Journal of Applied Mathematics and Computer Science 17, 217-232 (2007)
27. Lawrýnczuk, M., Tatjewski, P.: A computationally efficient nonlinear predictive control algorithm with RBF neural models and its application. In: Kryszkiewicz, M., Peters, J.F., Rybínski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol.4585, pp. 603-612. Springer, Heidelberg (2007)
28. Lawrýnczuk, M., Tatjewski, P.: An efficient nonlinear predictive control algorithm with neural models and its application to a high-purity distillation process. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., ?Zurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol.4029, pp. 76-85. Springer, Heidelberg (2006)
29. Maciejowski, J.M.: Predictive control with constraints. Prentice-Hall, Englewood Cliffs (2002)
30. Marlin, T.E.: Process control. McGraw Hill, New York (1995)
31. Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M.: Constrained model predictive control: stability and optimality. Automatica 36, 789-814 (2000)
32. Mayne, D.Q., Michalska, H.: Receding horizon control of nonlinear systems. IEEE Transactions on Automatic Control 35(7), 814-824 (1990)
33. Da Cruz Meleiro, L.A., Jośe, F., Zuben, V., Filho, R.M.: Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process. Engineering Applications of Artificial Intelligence (in press, 2009)
34. Michalska, H., Mayne, D.Q.: Robust receding horizon control of constrained nonlinear systems. IEEE Transactions on Automatic Control 38, 1623-1633 (1993)
35. Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667-682 (1999)
36. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1, 4-26 (1990)
37. Norgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural networks for modelling and control of dynamic systems. Springer, London (2000)
38. Parisini, T., Sanguineti, M., Zoppoli, R.: Nonlinear stabilization by recedinghorizon neural regulators. International Journal of Control 70, 341-362 (1998)
39. Pearson, R.K.: Selecting nonlinear model structures for computer control. Journal of Process Control 13, 1-26 (2003)
40. Peng, H., Yang, Z.J., Gui, W., Wu, M., Shioya, H., Nakano, K.: Nonlinear system modeling and robust predictive control based on RBF-ARX model. Engineering Applications of Artificial Intelligence 20, 1-9 (2007)
41. Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M.: Nonlinear model predictive control using neural networks. IEEE Control System Magazine 20, 56-62 (2000)
42. Pottmann, M., Seborg, D.E.: A nonlinear predictive control strategy based on radial basis function models. Computers and Chemical Engineering 21, 965-980 (1997)
43. Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733-764 (2003)
44. Rossiter, J.A.: Model-based predictive control. CRC Press, Boca Raton (2003)
45. Scattolini, R., Bittanti, S.: On the choice of the horizon in long-range predictive control - some simple criteria. Automatica 26, 915-917 (1990)
46. Scokaert, P.O.M., Mayne, D.Q., Rawlings, J.B.: Suboptimal model predictive control (feasibility implies stability). IEEE Transactions on Automatic Control 3, 648-654 (1999)
47. Tatjewski, P.: Advanced control of industrial processes, structures and algorithms. Springer, London (2007)
48. Tatjewski, P., Lawrýnczuk, M.: Soft computing in model-based predictive control. International Journal of Applied Mathematics and Computer Science 16, 101-120 (2006)
49. Trajanoski, Z., Wach, P.: Neural predictive control for insulin delivery using the subcu-taneous route. IEEE Transactions on Biomedical Engineering 45, 1122-1134 (1998) (Pubitemid 28385732)
50. Yu, D.L., Yu, D.W., Gomm, J.B.: Neural model adaptation and predictive control of a chemical process rig. IEEE Transactions on Control Systems Technology 14, 828-840 (2006)
51. Yu, D.L., Gomm, J.B.: Implementation of neural network predictive control to a multivariable chemical reactor. Control Engineering Practice 11, 1315-1323 (2003)
52. Zamarrẽno, J.M., Vega, P., Garcia, L.D., Francisco, M.: State-space neural network for modelling prediction and control. Control Engineering Practice 8, 1063-1075 (2000)