PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

An adaptive identification method based on the modulating functions technique and exact state observers for modeling and simulation of a nonlinear MISO glass melting process

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents new concepts of the identification method based on modulating functions and exact state observers with its application for identification of a real continuous-time industrial process. The method enables transformation of a system of differential equations into an algebraic one with the same parameters. Then, these parameters can be estimated using the least-squares approach. The main problem is the nonlinearity of the MISO process and its noticeable transport delays. It requires specific modifications to be introduced into the basic identification algorithm. The main goal of the method is to obtain on-line a temporary linear model of the process around the selected operating point, because fast methods for tuning PID controller parameters for such a model are well known. Hence, a special adaptive identification approach with a moving window is proposed, which involves using on-line registered input and output process data. An optimal identification method for a MISO model assuming decomposition to many inner SISO systems is presented. Additionally, a special version of the modulating functions method, in which both model parameters and unknown delays are identified, is tested on real data sets collected from a glass melting installation.
Rocznik
Strony
739--757
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
  • Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland, wby@agh.edu.pl
  • Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland; Techglass Sp. z o.o., ul. Zygmunta Starego 124, 30-198 Kraków, Poland, mdrapala@agh.edu.pl, michal.drapala@techglass.pl
  • Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland, jbyrski@agh.edu.pl
Bibliografia
  • [1] Asiri, S.M. and Laleg-Kirati, T. (2017). Modulating functions-based method for parameters and source estimation in one-dimensional partial differential equations, Inverse Problems in Science and Engineering 25(8): 1191–1215.
  • [2] Ayla, L. and Solis, J. (1991). Structured logic control in glass preparation processes, IEEE Transactions on Industry Applications 27(1): 108–111.
  • [3] Balestrino, A., Landi, A. and Sani, L. (2000a). Identification of Hammerstein systems with input/output time delay via modulating functions, IFAC Proceedings Volumes 33(23): 199–203.
  • [4] Balestrino, A., Landi, A. and Sani, L. (2000b). Parameter identification of continuous systems with multiple-input time delays via modulating functions, IEE Proceedings: Control Theory and Applications 147(1): 19–27.
  • [5] Byrski, J. and Byrski, W. (2012). The role of parameter constraints in EE and OE methods for optimal identification of continuous LTI models, International Journal of Applied Mathematics and Computer Science 22(2): 379–388, DOI: 10.2478/v10006-012-0028-3.
  • [6] Byrski, J. and Byrski, W. (2016). A double window state observer for detection and isolation of abrupt changes in parameters, International Journal of Applied Mathematics and Computer Science 26(3): 585–602, DOI: 10.1515/amcs-2016-0041.
  • [7] Byrski, J. and Byrski, W. (2018). An optimal identification of the input-output disturbances in linear dynamic systems by the use of the exact observation of the state, Mathematical Problems in Engineering: 1–15, Article ID 8048567, DOI: 10.1155/2018/8048567.
  • [8] Byrski, W. and Fuksa, S. (1995). Optimal identification of continuous systems in L2 space by the use of compact support filter, International Journal of Modelling & Simulation 15(4): 125–131.
  • [9] Byrski, W. and Kubiński, R. (1997). The convolution method for optimal identification generalized to MIMO continuous systems, Modelling, Identification and Control: Proceedings of the 16th IASTED International Conference, Innsbruck, Austria, pp. 44–47.
  • [10] Cieza, O.B., Tafur, J.C. and Reger, J. (2014). Frequency domain modulating functions for continuous-time identification of linear and nonlinear systems, 16th Latinamerican Control Conference, At Quintana Roo, Mexico, pp. 690–695.
  • [11] Co, T. and Ydstie, B. (1990). System identification using modulating functions and fast Fourier transforms, Computers & Chemical Engineering 14(10): 1051–1066.
  • [12] Gough, B.P. and Matovich, D. (1997). Predictive-adaptive temperature control of molten glass, IEEE Industry Applications Society Dynamic Modeling Control Applications for Industry Workshop, Vancouver, BC, Canada, pp. 51–55.
  • [13] Grega, W., Piłat, A. and Tutaj, A. (2015). Modelling of the glass melting process for real-time implementation, International Journal of Modeling and Optimization 5(6): 366–373.
  • [14] Grega, W., Tutaj, A., Klemiato, M. and Byrski, W. (2016). Comparison of real-time industrial process control solutions: Glass melting case study, 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, pp. 122–127.
  • [15] Janiczek, T. (2010). Generalization of the modulating functions method into the fractional differential equations, Bulletin of the Polish Academy of Sciences: Technical Sciences 58(4): 593–599.
  • [16] Jouffroy, J. and Reger, J. (2015). Finite-time simultaneous parameter and state estimation using modulating functions, IEEE Conference on Control Applications (CCA), Sydney, NSW, Australia, pp. 394–399.
  • [17] Khoury, R. and Harder, D. (2016). Numerical Methods and Modelling for Engineering, Springer, Cham.
  • [18] Kozłowski, J. and Kowalczuk, Z. (2015). On-line parameter and delay estimation of continuous-time dynamic systems, International Journal of Applied Mathematics and Computer Science 25(2): 223–232, DOI: 10.1515/amcs-2015-0017.
  • [19] Maletinsky, V. (1979). Identification of continuous dynamical systems with spline-type modulating functions method, IFAC Proceedings Volumes 12(8): 275–281.
  • [20] Pearson, A., Shen, Y. and Klein, V. (1994). Application of Fourier modulating functions to parameter estimation of a multivariable linear differential system, IFAC Proceedings Volumes 27(8): 1013–1018.
  • [21] Preisig, H. and Rippin, D. (1993). Theory and application of the modulating function method. I: Review and theory of the method and theory of the spline-type modulating functions, Computers & Chemical Engineering 17(1): 1–16.
  • [22] Rao, G.P., Diekmann, K. and Unbenhauen, H. (1984). Parameter estimation in large scale interconnected systems, IFAC Proceedings Volumes 17(2): 729–733.
  • [23] Rao, G.P. and Sivakumar, G. (1979). Identification of deterministic time-lag systems, IEEE Transactions on Automatic Control 21(4): 527–529.
  • [24] Rao, G.P. and Unbehauen, G. (2006). Identification of continous-time systems, IEE Proceedings: Control Theory and Applications 153(2): 185–220.
  • [25] Shinbrot, M. (1957). On the analysis of linear and nonlinear systems, Transactions of the American Society of Mechanical Engineers: Journal of Basic Engineering 79: 547–552.
  • [26] Wang, Q., Chalaye, G., Thomas, G. and Gilles, G. (1997). Predictive control of a glass process, Control Engineering Practice 5(2): 167–173.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f2c87f3d-615b-456b-ab04-cdd48c3c8dfc
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.