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Effectiveness of Dynamic Matrix Control algorithm with Laguerre functions

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Języki publikacji
EN
Abstrakty
EN
The paper is concerned with the presentation and analysis of the Dynamic Matrix Control (DMC) model predictive control algorithm with the representation of the process input trajectories by parametrised sums of Laguerre functions. First the formulation of the DMCL (DMC with Laguerre functions) algorithm is presented. The algorithm differs from the standard DMC one in the formulation of the decision variables of the optimization problem - coefficients of approximations by the Laguerre functions instead of control input values are these variables. Then the DMCL algorithm is applied to two multivariable benchmark problems to investigate properties of the algorithm and to provide a concise comparison with the standard DMC one. The problems with difficult dynamics are selected, which usually leads to longer prediction and control horizons. Benefits from using Laguerre functions were shown, especially evident for smaller sampling intervals.
Rocznik
Strony
795--814
Opis fizyczny
Bibliogr. 20 poz., rys., wzory
Twórcy
  • Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warszawa, Poland
Bibliografia
  • [1] T.L. Blevins, G.K. McMillan, W.K. Wojsznis, and M.W. Brown: Advanced Control Unleashed. The ISA Society, Research Triangle Park, NC, 2003.
  • [2] T.L. Blevins,W.K. Wojsznis and M.Nixon: Advanced Control Foundation. The ISA Society, Research Triangle Park, NC, 2013.
  • [3] E.F. Camacho and C. Bordons: Model Predictive Control. Springer Verlag, London, 1999.
  • [4] M. Ławrynczuk: Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach, Studies in Systems, Decision and Control. Vol. 3. Springer Verlag, Heidelberg, 2014.
  • [5] M. Ławrynczuk: Nonlinear model predictive control for processes with complex dynamics: parametrisation approach using Laguerre functions. International Journal of Applied Mathematics and Computer Science, 30(1), (2020), 35-46, DOI: 10.34768/amcs-2020-0003.
  • [6] J.M. Maciejowski: Predictive Control. Prentice Hall, Harlow, England, 2002.
  • [7] R. Nebeluk and P. Marusak: Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors. Archives of Control Sciences, 30(2), (2020), 325-363, DOI: 10.24425/acs.2020.133502.
  • [8] S.J. Qin and T.A. Badgwell: Asurvey of industrial model predictive control technology. Control Engineering Practice, 11(7), (2003), 733-764, DOI: 10.1016/S0967-0661(02)00186-7.
  • [9] J. B. Rawlings and D. Q. Mayne: Model Predictive Control: Theory and Design. Nob Hill Publishing, Madison, 2009.
  • [10] J.A. Rossiter: Model-Based Predictive Control. CRC Press, Boca Raton - London - New York - Washington, D.C., 2003.
  • [11] P. Tatjewski: Advanced Control of Industrial Processes. Springer Verlag, London, 2007.
  • [12] P. Tatjewski: Advanced control and on-line process optimization in multilayer structures. Annual Reviews in Control, 32(1), (2008), 71-85, DOI: 10.1016/j.arcontrol.2008.03.003.
  • [13] P. Tatjewski: Disturbance modeling and state estimation for offset-free predictive control with state-spaced process models. International Journal of Applied Mathematics and Computer Science, 24(2), (2014), 313-323, DOI: 10.2478/amcs-2014-0023.
  • [14] P. Tatjewski: Offset-free nonlinear Model Predictive Control with state-space process models. Archives of Control Sciences, 27(4), (2017), 595-615, DOI: 10.1515/acsc-2017-0035.
  • [15] P. Tatjewski: DMC algorithm with Laguerre functions. In Advanced, Contemporary Control, Proceedings of the 20th Polish Control Conference, pages 1006-1017, Łódź, Poland, (2020).
  • [16] G. Valencia-Palomo and J.A. Rossiter: Using Laguerre functions to improve efficiency of multi-parametric predictive control. In Proceedings of the 2010 American Control Conference, Baltimore, (2010).
  • [17] B. Wahlberg: System identification using the Laguerre models. IEEE Transactions on Automatic Control, 36(5), (1991), 551-562, DOI: 10.1109/9.76361.
  • [18] L. Wang: Discrete model predictive controller design using Laguerre functions. Journal of Process Control, 14(2), (2004), 131-142, DOI: 10.1016/S0959-1524(03)00028-3.
  • [19] L. Wang: Model Predictive Control System Design and Implementation using MATLAB. Springer Verlag, London, 2009.
  • [20] R. Wood and M. Berry: Terminal composition control of a binary distillation column. Chemical Engineering Science, 28(9), (1973), 1707-1717, DOI: 10.1016/0009-2509(73)80025-9.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-7ff8d1c0-71f1-41c7-ba0b-3c93c48ef043
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