PREDICTIVE CONTROL OF CHAOTIC DISCRETE PLANTS

Öz

PREDICTIVE CONTROL OF CHAOTIC DISCRETE PLANTS

Anahtar Kelimeler

• Neural network control, Multi Layer Perceptron, Local Linear Model Tree, predictive control, Chaos

Kaynakça

1. Camacho, E.F. Model predictive control, Springer Verlag, 1998.
2. Garcia, C.E., Prett, D.M., and Morari, M. Model predictive control: theory and practice- a survey, Automatica, 25(3), pp.335-348, 1989.
3. Badgwell, A.B., Qin, S.J. Review of nonlinear model predictive control applications, In Nonlinear predictive control theory and practice, Kouvaritakis, B, Cannon, M (Eds.), IEE Control Series, pp.3-32, 2001.
4. Parker, R.S., Gatzke E.P., Mahadevan, R., Meadows, E.S., and Doyle, F.J. Nonlinear model predictive control: issues and applications, In Nonlinear predictive control theory and practice, Kouvaritakis, B, Cannon, M (Eds.), IEE Control Series, pp.34-57, 2001.
5. Babuska, R., Botto, M.A., Costa, J.S.D., and Verbruggen, H.B. Neural and fuzzy modeling on nonlinear predictive control, a comparison study, Computatioinal Engineering in Systems Science, July, 1996.
6. Nelles, O. Nonlinear system identification: from classical approach to neuro-fuzzy identification, Springer Verlag, 2001.
7. Narendra, K. S., and Parthasarathy, K., Identification and control of dynamic systems using neural networks. IEEE Transactions on Neural Networks, 1, pp.4–27, 1990.
8. Arahal, M.R., Berenguel, M., and Camacho, E.F. Neural identification applied to predictive control of a solar plant, Con. Eng. Prac. 6(3), pp.333-344, 1998.

9. Lennox, B., and Montague, G. Neural network control of a gasoline engine with rapid sampling, In Nonlinear predictive control theory and practice, Kouvaritakis, B, Cannon, M (Eds.), IEE Control Series, pp.245-255, 2001.
10. Petrovic, I., Rac, Z., and Peric, N. Neural network based predictive control of electrical drives with elastic transmission and backlash, Proc. EPE2001, Graz, Austria, 2001.
11. Tan, Y. and Cauwenberghe, A. Non-linear one step ahead control using neural networks: control strategy and stability design, Automatica (12), pp.1701-1706, 1996.
12. Temeng, H., Schenelle, P. and McAvoy, T. Model predictive control of an industrial packed bed reactor using neural networks, J. Proc. Control 5(1), pp.19-28, 1995.
13. Zamarrano, J.M., Vega, P. Neural predictive control. Application to a highly nonlinear system, Engineering Application of Artificial Intelligence, 12(2), pp.149-158, 1999.
14. Draeger, A., Engel, S., and Ranke, H., Model predictive control using neural networks, IEEE Control System Magazine, 15, pp.61–66, Gomm, J. B., Evans, J. T., and Williams, D., Development and performance of a neural network predictive controller. Control Engineering Practice, 5(1), pp.49–60, 1997.
15. Diyaz, G., Sen, M., Yang, K.T., McClain, R.L., Simulation of heat exchanger performance by artificial neural networks, Int. J. HVAC and R Res., 5 (3), pp.195-208, 1999.
16. Ayoubi, M., Dynamic multi-layer perception networks: application to the nonlinear identification and predictive control of a heat exchanger, in: Applications of Neural Adaptive Control Technology, World Scientific series in Robotics and Intelligent Systems, 17, pp.205- , 1997.
17. Renotie, C., Wouwer, A.V. and Remy, M. Neural modeling and control of a heat exchanger based on SPSA techniques, Proc. American Control Conference, Illinois, pp.3299-3303,
18. Bittanti, S. and Piroddi, L. Nonlinear identification and control of a heat exchanger: a neural network approach, Journal of the Franklin Institute, 1996.
19. Lim, K.W. and Ling, K.V. generalized predictive control of a heat exchanger, IEEE Control System Magazine, pp.9-12, 1989.
20. Parte, M.P. and Camacho, E.F. Application of a predictive sliding mode controller to a heat exchanger, Application, Glasgow, Scotland, pp.1219-1224,
21. Montague, G.A., Willis, M.J., Tham, M.T., Morris, A.J., (1991). Artificial neural network based control. International Conference on Control, pp.266–271.
22. Skrijave, I. and Matko, D. Predictive functional control based on fuzzy model for heat exchanger pilot plant, IEEE Trans. Fuzzy Systems, 8(6), pp.705-812, 2000.
23. Nelles, O. and R. Isermann (1996). Basis function networks for interpolation of local linear models. In: IEEE Conference on Decision and Control (CDC). Kobe, Japan. pp. 470–475.
24. Liu, G.P., 2001, Nonlinear Identification and Control: A Neural Network Approach, Springer.
25. Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J., (1992). Neural networks for control system—A survey. Automatica, 28, pp.1083–1112.
26. Takahashi, Y., (1993). Adaptive predictive control of nonlinear time varying system using neural network, in Proc. IEEE International
27. Symposium on Neural Networks, pp.1464–1468.
28. Chen, G. and Moiola, J. L. An overview of bifurcation, chaos, and nonlinear dynamics in nonlinear system, J. Franklin Inst., vol. 331B, pp. 858, 1994.
29. Ogorzalek, M. J. Taming chaos, Part two: control, IEEE Trans. Circuits Syst. I, vol. 40, pp. 706, 1993.
30. Chen, G. and Done, X. From chaos to order: methodologies, perspectives and applications, World Scientific, Singapore, 1998.
31. Fradkov, A. L. and Pogromsky, A. Y. Introduction to control of oscillations and chaos, World Scientific, Singapore, 1998.