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Abstract

The paper presents methods and algorithms for automatic control based on predictive models of dynamic objects in continuous and semi-continuous industrial production. The approach under consideration allows further improvement of scientific foundations and formalized methods for designing the implementation of highly efficient systems for advanced control of complex technological processes and industrial production in real-time.

First Page

78

Last Page

85

References

1. Skogestad, S., Postlethwaite, I. (2005). Multivariate Feedback Control. John Wiley&Sons, 608 p.

2. Al-Ghazzawi, A., Ali, E., Nouh, A., Zafiriou, E. (2001). On-line tuning strategy for model predictive controllers. Journal of Process Control, 11(3), 265-284.

3. Rotach, V.Ya. (2004). Teoriya avtomaticheskogo upravleniya [The theory of automatic control]. M.: Izd-vo MEI, 400 p. (in Russian).

4. Albin, T., Ritter, D., Abel, D., Liberda, N., Quirynen, R., Diehl, M. (2015). Nonlinear MPC for a two-stage turbocharged gasoline engine airpath. IEEE 54th Annual Conference on Decision and Control. 849-856.

5. Razvan, C., Livint, G. (2015). Nonlinear model predictive control of autonomous vehicle steering. 19th International Conference on System Theory and Computing. 466-471.

6. Parisio, A., Rikos, E., Glielmo, L. (2016). Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study. Journal of Process Control. 43. 24-37.

7. Grosso, J.M., Ocampo-Martínez, C., Puig, V. (2014). Chance-constrained model predictive control for drinking water networks. Journal of Process Control. 24(5). 504-516.

8. O’Dwyer, E., Tommasi, L.D., Kouramas, K. (2017). Prioritised objectives for model predictive control of building heating systems. Control Engineering Practice. 63. 57-68.

9. Hartley, E.N., Trodden, P.A., Richards, A.G. (2012). Model predictive control system design and implementation for spacecraft rendezvous. Control Engineering Practice. 20. 695-713.

10. Kothare, M.V., Balakrishnan, V., Morari, M. (1996). Robust constrained model predictive control using linear matrix inequalities. Automatica. 32(10). 1361-1379.

11. Zheng, Z.Q., Morari, M. (1993). Robust Stability of Constrained Model Predictive. Proceedings of the American Control Conference, session WM7(San Francisco). 379-383.

12. Li, J., Wu, C., Li, S. (2014). Optimal disturbance rejection control approach based on a compound neural network prediction method. Journal of Process Control. 24. 1516-1526.

13. Gorges, D. (2017). Relations between Model Predictive Control and Reinforcement Learning. IFAC-PapersOnLine. 50(1). 4920-4928.

14. Yusupbekov, N.R., Gulyamov, S.M., Doshchanova, M.Y. (2019). Neural identification of a dynamic model of a technological process. 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent. 1-5.

15. Abonyi, J., Bódizs, Á., Nagy, L., Szeifert, F. (2012). Predictor Corrector Controller using Wiener Fuzzy Convolution Model. Hungarian Journal of Industrial Chemistry, 27(3), 227-233.

16. Azhar, A.S.S., Al-Duwais,h H.N. (2002). Identification of Wiener Model Using Radial Basis Functions Newral Networks. J.R. Dorronsoro (Ed.):ICANN 2002, LNCS 2415, 344-350.

17. Lawrynczuk, M. (2010). Computationally efficient nonlinear predictive control based on neural Wiener models. Neurocomputing. 74, 401-417.

18. Yousef, A.M. (2012). Neural Network Predictive Control Based Power System Stabilizer. Research Journal of Applied Sciences, Engineering and Technology, 4(8), 995-1003.

19. Lu, C.H., Tsai, C. (2007). Generalized predictive control using recurrent fuzzy neural networks for industrial processes. Journal of Process Control, 17, 83-92.

20. Seyab, R., Cao, Y. (2008). Differential Recurrent Neural Network based Predictive Control. Computers and Chemical Engineering, 32(7), 1533-1545.

21. Lepetič, M., Škrjanc, I., Hector, G. Ch., Drago, M. (2003). Predictive functional control based on fuzzy model: magnetic suspension system case study. Engineering Applications of Artificial Intelligence, 16, 425-430.

22. Huang, Y.L., Lou, H.H., Gong, J.P., Edgar, T.F. (2000). Fuzzy Model Predictive Control. IEEE Transactions on Fuzzy Systems, 8(6).

23. Atanassov, K. (1999). Intuitionistic Fuzzy Sets. Springern, Hielderberg.

24. Clarke D.W., Mohtadi C., Tuffs P.S. Generalized Predictive Control. Automatica, Vol. 23, Issue 2, 1987. pp. 137-148.

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