SavedAdd your journal

Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2007 / Cilt: 7 - Sayı: 1

Predictive control of chaotic discrete plants

S. Shams Abbad FARAHANI
M.A. NEKOUI
Pages
277–285
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The effectiveness of the purposed methodology is shown through simulation results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper deals with identification and predictive control of a nonlinear chaotic discrete plant.
📊
Key Findings & Data:The main difficulties for identification and control of this plant arise from the strongly nonlinear center and its chaotic behavior.
💡
Conclusion & Impact:The effectiveness of the purposed methodology is shown through simulation results.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This paper deals with identification and predictive control of a nonlinear chaotic discrete plant. The main difficulties for identification and control of this plant arise from the strongly nonlinear center and its chaotic behavior. First, an internal feedback is applied to suppress the chaotic behavior. Then, a neural network based predictive controller using Multi Layer Perceptron (MLP) is designed to govern the dynamics of this plant. The effectiveness of the purposed methodology is shown through simulation results.

🔖

Cite This Article

FARAHANI, S. S. A., NEKOUI, M. (2022). Predictive control of chaotic discrete plants. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 7 - Sayı: 1, 277–285. https://ajindex.com/articles/543784

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  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 32(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, 1995
  15. Gomm, J. B., Evans, J. T., and Williams, D., Development and performance of a neural network p