Predictive control of the iron ore beneficiation process based on the hammerstein hybrid model

• Autorzy: Porkuian Olga , Morkun Vladimir , Morkun Natalia

Identyfikatory

DOI

10.2478/ama-2019-0036

• Języki publikacji: EN

Abstrakty

EN

Non-linear, dynamic, non-stationary properties characterize objects of the iron ore beneficiation line. Therefore, for their approximation, it is advisable to use models of the Hammerstein class. As a result of comparing the three models of Hammerstein: simple, parallel and recursive-parallel, it was shown that the best result for identifying the considered processes of magnetic beneficiation of iron ore by the minimum error criterion was obtained using the Hammerstein recursive-parallel model. Hence, it is recommended for the identification of beneficiation production objects.

Słowa kluczowe

EN

predictive control; iron ore; beneficiation; Hammerstein model; recursive-parallel model

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2019
• Tom: Vol. 13, no. 4
• Strony: 262--270
• Opis fizyczny: Bibliogr. 45 poz., rys., wykr.

Twórcy

autor

Porkuian Olga

• Faculty of Information Technology and Electronics,Department of Programming and Mathematics, Volodymyr Dahl East Ukrainian National University, pr. Central 59-а, Severodonetsk, 93400, Ukraine

autor

Morkun Vladimir

• Faculty of Information Technologies, Computer Science and Technology Department, Kryvyi Rih National University, 11 Vitaliy Matusevych St., Kryvyi Rih, 50027, Ukraine

autor

Morkun Natalia

• Faculty of Information Technologies, Computer Science and Technology Department, Kryvyi Rih National University, 11 Vitaliy Matusevych St., Kryvyi Rih, 50027, Ukraine

Bibliografia

• 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.
• 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.
• 3. Babuska R. (1998), Fuzzy Modeling for Control, Kluwer Academic Publishers, Boston.
• 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.
• 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.
• 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.
• 7. Chen J., Wang X. (2015), Identification of Hammerstein systems with continuous nonlinearity, Information Processing Letters, 115(11), 822–827.
• 8. Clarke D. W., Tuffs P.S., Mothadi C. (1989), Generalized predictive control – part I. the basic algorithm, Automatica, 23, 137–148.
• 9. Clarke D.W., Mohtadi C. (1989), Properties of generalized predictive control, Automatica, 25(6), 859–875.
• 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.
• 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.
• 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.
• 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), 1717–1727
• 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),
• 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),
• 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.
• 17. Kazuo T., Wang H.O. (2001), Fuzzy Control Systems Design And Analysis, John Wiley&Sons.
• 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), 386–396
• 19. Leontaritis I.J. Billings S.A. (1987), Experimental design and identifiably for nonlinear systems, International Journal of Systems Science,18, 189–202.
• 20. Li Yu., Shchetsen M. (1968), Opredeleniye yader Vinera-Khopfa metodom vzaimnoy korrelyatsii. Tekhnicheskaya kibernetika za rubezhom [Determination of Wiener-Hopf kernels by crosscorrelation. Technical cybernetics abroad], Mashinostroyeniye, Moskow (In Russian),
• 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/acs
• 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.
• 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.
• 24. Morkun V., Morkun N., Pikilnyak A. (2014b), Ultrasonic facilities for the ground materials characteristics control, Metallurgical and Mining Industry, 6(2), 31–35.
• 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.
• 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.
• 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.
• 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.
• 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.
• 30. Morkun V., Tcvirkun S. (2014), Investigation of methods of fuzzy clustering for determining ore types, Metallurgical and Mining Industry, 5, 11–14.
• 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.
• 32. Ozer S., Zorlu H., Mete S. (2016), System identification application using Hammerstein model, Sadhana, 41(6), 597–605.
• 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.
• 34. Postlethwaite B.E. (1996), Building a model-based fuzzy controller, Fuzzy Sets and Systems, 79, 3–13.
• 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.
• 37. Rossiter J.A. (2003), Model-Based Predictive Control: a Practical Approach, CRC Press.
• 38. Sanches J.M.M., Rodellar J. (1996), Adaptive predictive control: from the concepts to plant optimization, Prentice Hall International (UK) Limited.
• 39. Sjoberg J., Zhang Q., Ljung L., Benveniste A., Deylon B., Glorennec P-Y., Hjalmarsson H., Juditsky A. (1995), Nonlinear blackbox modeling in system identification: a unified overview, Automatica, 31, 1691–1724.
• 40. Stoica P. (1981), On the convergence of an iterative algorithm used for Hammerstein system identification, IEEETrans. Automatic Control, 26, 967–969.
• 41. Tobi T., Hanafusa T. (1991), A practical application of fuzzy control for an airconditioning system, International Journal of Approximate Reasoning, 5, 331–348.
• 42. Verhaegen M., Westwick D. (1996), Identifying MIMO Hammerstein systems in the context of subspace model identification, International Journal of Control, 63, 331–349.
• 43. Wills A., Ninness B. (2012), Generalised Hammerstein–Wiener system estimation and a benchmark application, Control Engineering Practice, 20(11), 1097–1108.
• 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.
• 45. Yucai Z. (1999), Parametric Wiener model identification for control, Proceedings IFAC World Congress, Bejing, China, 3a–02–1, 34–46.
• 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),

Uwagi

Błędna numeracja bibliografii (brak nr 35)

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).