Accès libre

Predictive Control of the Iron Ore Beneficiation Process Based on the Hammerstein Hybrid Model

À propos de cet article

Citez

1. Abba S.I., Nourani V., Elkiran G. (2019), Multi-parametric modeling of water treatment plant using AI-based non-linear ensemble, Aqua, 68 (7), 547–561.10.2166/aqua.2019.078Search in Google Scholar

2. Abonyi J., Nagy L., Szeifert F. (2000), Hybrid fuzzy convolution modelling and identification of chemical process systems, International Journal of Systems Science, 31, 457–466.10.1080/002077200291046Search in Google Scholar

3. Babuska R. (1998), Fuzzy Modeling for Control, Kluwer Academic Publishers, Boston.Search in Google Scholar

4. Babuska R., Roubos J.A., Verbruggen H.B. (1998), Identification of MIMO systems by input-output TS fuzzy models, Proceedings FUZZ-IEEE’98, Anchorage, Alaska, 57–69.Search in Google Scholar

5. Botto M. Ayala, van den Boom T.J.J., Krijgsman A., S’a da J. (1999), Costa Constrained nonlinear predictive control based on input-output linearization using a neural network, International Journal of Control, 72(17), 1538–1554.10.1080/002071799220038Search in Google Scholar

6. Chen H., Ding F. (2015), Hierarchical least-squares identification for Hammerstein nonlinear controlled autoregressive systems, Circuits, Systems and Signal Processing, 34(1), 61–75.10.1007/s00034-014-9839-9Search in Google Scholar

7. Chen J., Wang X. (2015), Identification of Hammerstein systems with continuous nonlinearity, Information Processing Letters, 115(11), 822–827.10.1016/j.ipl.2015.06.004Search in Google Scholar

8. Clarke D. W., Tuffs P.S., Mothadi C. (1989), Generalized predictive control – part I. the basic algorithm, Automatica, 23, 137–148.10.1016/0005-1098(87)90087-2Search in Google Scholar

9. Clarke D.W., Mohtadi C. (1989), Properties of generalized predictive control, Automatica, 25(6), 859–875.10.1016/0005-1098(89)90053-8Search in Google Scholar

10. Falck T., Dreesen P., Brabanter K., Pelckmans K., De Moor B., Suykens J.A.K. (2012), Least-Squares Support Vector Machines for the identification of Wiener–Hammerstein systems, Control Engineering Practice, 20(11), 1165–1174.10.1016/j.conengprac.2012.05.006Search in Google Scholar

11. Fruzetti K.P., Palazoglu A., McDonald K.A. (1997), Nonlinear model predictive control using Hammerstein models, Journal of Process Control, 7(1), 31–41.10.1016/S0959-1524(97)80001-BSearch in Google Scholar

12. Garcia C.E., Morari M. (1982), Internal model control: 1.A unifying review and some new results, Ind. Eng. Chem. Process Res. Dev, 21, 308–323.Search in Google Scholar

13. Ikhouane F., Giri F. (2014), A unified approach for the parametric identification of SISO/MIMO Wiener and Hammerstein systems, Journal of the Franklin Institute, 351(3), 1717–172710.1016/j.jfranklin.2013.12.016Search in Google Scholar

14. Ivanov A.I. (1991), Ortogonalnaya identifikatsiya nelineynykh dinamicheskikh sistem s konechnoy i beskonechnoy pamyatyu pri odnom i neskol’kikh vkhodakh [Orthogonal identification of nonlinear dynamical systems with finite and infinite memory at one or several inputs], NIKIRET, Penza (In Russian),Search in Google Scholar

15. Ivanov A.I. (1995), Bystryye algoritmy sinteza nelineynykh dinamicheskikh modeley po eksperimental’nym dannym [Fast algorithms for the synthesis of nonlinear dynamic models from experimental data], NPF “Kristall”, Penza (In Russian),Search in Google Scholar

16. Junhao Shi, Sun H.H. (1990), Nonlinear system identification for cascaded block model: an application to electrode polarisation impedance, IEEE Trans. Biomed. Eng, 6, 574–587.Search in Google Scholar

17. Kazuo T., Wang H.O. (2001), Fuzzy Control Systems Design And Analysis, John Wiley&Sons.Search in Google Scholar

18. Le F., Markovsky I., Freeman C.T., Rogers E. (2012), Recursive identification of Hammerstein systems with application to electrically stimulated muscle, Control Engineering Practice, 20(4), 386–39610.1016/j.conengprac.2011.08.001Search in Google Scholar

19. Leontaritis I.J. Billings S.A. (1987), Experimental design and identifiably for nonlinear systems, International Journal of Systems Science,18, 189–202.10.1080/00207728708963958Search in Google Scholar

20. Li Yu., Shchetsen M. (1968), Opredeleniye yader Vinera-Khopfa metodom vzaimnoy korrelyatsii. Tekhnicheskaya kibernetika za rubezhom [Determination of Wiener-Hopf kernels by cross-correlation. Technical cybernetics abroad], Mashinostroyeniye, Moskow (In Russian),Search in Google Scholar

21. Ma J., Ding F., Xiong W., Yang E. (2016), Combined state and parameter estimation for Hammerstein systems with time-delay using the Kalman filtering, International Journal of Adaptive Control and Signal Processing, 00:1–17. DOI: 10.1002/acsSearch in Google Scholar

22. Mete S., Ozer S., Zorlu H. (2016), System identification using Hammerstein model optimized with differential evolution algorithm, AEU - International Journal of Electronics and Communications, 70(12), 1667–1675.10.1016/j.aeue.2016.10.005Search in Google Scholar

23. Morkun V., Morkun N., Pikilnyak A. (2014a), Ultrasonic phased array parameters determination for the gas bubble size distribution control formation in the iron ore flotation, Metallurgical and Mining Industry, 6(3), 28–31.Search in Google Scholar

24. Morkun V., Morkun N., Pikilnyak A. (2014b), Ultrasonic facilities for the ground materials characteristics control, Metallurgical and Mining Industry, 6(2), 31–35.Search in Google Scholar

25. Morkun V., Morkun N., Pikilnyak A. (2014c), The adaptive control for intensity of ultrasonic influence on iron ore pulp, Metallurgical and Mining Industry, 6, 8–11.Search in Google Scholar

26. Morkun V., Morkun N., Pikilnyak A. (2015c), Adaptive control system of ore beneficiation process based on Kaczmarz projection algorithm, Metallurgical and Mining Industry, 2, 35–38.Search in Google Scholar

27. Morkun V., Morkun N., Tron V. (2015a), Formalization and frequency analysis of robust control of ore beneficiation technological processes under parametric uncertainty, Metallurgical and Mining Industry, 5, 7–11.Search in Google Scholar

28. Morkun V., Morkun N., Tron V. (2015b), Model synthesis of nonlinear nonstationary dynamical systems in concentrating production using Volterra kernel transformation, Metallurgical and Mining Industry, 10, 6–9.Search in Google Scholar

29. Morkun V., Morkun N., Tron V., Hryshchenko S. (2018), Synthesis of robust controllers for the control systems of technological units as iron ore processing plants, Eastern European Journal of Enterprise Technologies, 1(2-91), 37–47.10.15587/1729-4061.2018.119646Search in Google Scholar

30. Morkun V., Tcvirkun S. (2014), Investigation of methods of fuzzy clustering for determining ore types, Metallurgical and Mining Industry, 5, 11–14.Search in Google Scholar

31. Narendra K.S., Gallman P.G. (1966), An iterative method for the identification of nonlinear systems using the Hammerstein model, IEEETrans. Automatic Control, 12, 546.10.1109/TAC.1966.1098387Search in Google Scholar

32. Ozer S., Zorlu H., Mete S. (2016), System identification application using Hammerstein model, Sadhana, 41(6), 597–605.10.1007/s12046-016-0505-8Search in Google Scholar

33. Piroddi L., Farina M., Lovera M. (2012), Black box model identification of nonlinear input–output models: A Wiener–Hammerstein benchmark, Control Engineering Practice, 20(11), 1109–1118.10.1016/j.conengprac.2012.03.002Search in Google Scholar

34. Postlethwaite B.E. (1996), Building a model-based fuzzy controller, Fuzzy Sets and Systems, 79, 3–13.10.1016/0165-0114(95)00287-1Search in Google Scholar

36. Rébillat M., Hennequin R., Corteel E., Katz B. (2010), Identification of cascade of Hammerstein models for the description of nonlinearities in vibrating devices, Journal of Sound and Vibration, 330(5), 1018–1038.10.1016/j.jsv.2010.09.012Search in Google Scholar

37. Rossiter J.A. (2003), Model-Based Predictive Control: a Practical Approach, CRC Press.Search in Google Scholar

38. Sanches J.M.M., Rodellar J. (1996), Adaptive predictive control: from the concepts to plant optimization, Prentice Hall International (UK) Limited.Search in Google Scholar

39. Sjoberg J., Zhang Q., Ljung L., Benveniste A., Deylon B., Glorennec P-Y., Hjalmarsson H., Juditsky A. (1995), Nonlinear black-box modeling in system identification: a unified overview, Automatica, 31, 1691–1724.10.1016/0005-1098(95)00120-8Search in Google Scholar

40. Stoica P. (1981), On the convergence of an iterative algorithm used for Hammerstein system identification, IEEETrans. Automatic Control, 26, 967–969.10.1109/TAC.1981.1102761Search in Google Scholar

41. Tobi T., Hanafusa T. (1991), A practical application of fuzzy control for an airconditioning system, International Journal of Approximate Reasoning, 5, 331–348.10.1016/0888-613X(91)90016-FSearch in Google Scholar

42. Verhaegen M., Westwick D. (1996), Identifying MIMO Hammerstein systems in the context of subspace model identification, International Journal of Control, 63, 331–349.10.1080/00207179608921846Search in Google Scholar

43. Wills A., Ninness B. (2012), Generalised Hammerstein–Wiener system estimation and a benchmark application, Control Engineering Practice, 20(11), 1097–1108.10.1016/j.conengprac.2012.03.011Search in Google Scholar

44. Young A.D. (1977), State of the art and trends in computers and control equipment, 2-nd IFAC Symp. “Automat. Mining, Miner. and Metal. Proc.”, Pretoria, 41–46.Search in Google Scholar

45. Yucai Z. (1999), Parametric Wiener model identification for control, Proceedings IFAC World Congress, Bejing, China, 3a–02–1, 34–46.Search in Google Scholar

46. Zubov D.A. (2006), Passifikatsiya i sintez algoritma avtomaticheskogo upravleniya odnim klassom SISO-obyektov ugleobogashcheniya s ispolzovaniyem algebry Li [Passification and synthesis of an automatic control algorithm for one class of SISO objects for coal enrichment using Lie algebra], Zbagachennya korisnikh kopalin, 24(65), 80–87 (In Russian),Search in Google Scholar