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Nonlinear model predictive control for processes with complex dynamics: A parameterisation approach using Laguerre functions

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.
Rocznik
Strony
35--46
Opis fizyczny
Bibliogr. 32 poz., tab., wykr.
Twórcy
  • Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
  • [1] Bemporad, A., Morari,M., Dua, V. and Pistikopoulos, E. (2002). The explicit linear quadratic regulator for constrained systems, Automatica 38(1): 3–20.
  • [2] Bosschaerts, W., Van Renterghem, T., Hasan, O.A. and Limam, K. (2017). Development of a model based predictive control system for heating buildings, Energy Procedia 122(1): 519–528.
  • [3] Chaber, P. and Ławryńczuk, M. (2019). Fast analytical model predictive controllers and their implementation for STM32 ARM microcontroller, IEEE Transactions on Industrial Informatics 15(8): 4580–45900.
  • [4] Clarke, D.W., Mohtadi, C. and Tuffs, P.S. (1987). Generalized predictive control. Part I: The basic algorithm, Automatica 23(2): 137–148.
  • [5] Falconí, G.P., Angelov, J. and Holzapfel, F. (2018). Adaptive fault-tolerant position control of a hexacopter subject to an unknown motor failure, International Journal of Applied Mathematics and Computer Science 28(2): 309–321, DOI: 10.2478/amcs-2018-0022.
  • [6] Greblicki, W. (2010). Nonparametric input density-free estimation of the nonlinearity in Wiener systems, IEEE Transactions on Information Theory 56(7): 3575–3580.
  • [7] Gutiérrez-Urquídez, R.C., Valencia-Palomo, G., Rodríguez-Elias, O.M. and Trujillo, L. (2015). Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm, Applied Soft Computing 31(6): 326–338.
  • [8] Harrabi, N., Kharrat, M., Aitouche, A. and Souissi, M. (2018). Control strategies for the grid side converter in a wind generation system based on a fuzzy approach, International Journal of Applied Mathematics and Computer Science 28(2): 323–333, DOI: 10.2478/amcs-2018-0023.
  • [9] Jama, M., Wahyudie, A. and Noura, H. (2018). Robust predictive control for heaving wave energy converters, Control Engineering Practice 77(1): 138–149.
  • [10] Janczak, A. and Korbicz, J. (2019). Two-stage instrumental variables identification of polynomial Wiener systems with invertible nonlinearities, International Journal of Applied Mathematics and Computer Science 29(3): 571–580, DOI: 10.2478/amcs-2019-0042.
  • [11] Karimi Pour, F., Puig, V. and Ocampo-Martinez, C. (2018). Multi-layer health-aware economic predictive control of a pasteurization pilot plant, International Journal of Applied Mathematics and Computer Science 28(1): 97–110, DOI: 10.2478/amcs-2018-0007.
  • [12] Khan, B. and Rossiter, J. A. (2013). Alternative parameterisation within predictive control: A systematic selection, International Journal of Control 86(8): 1397–1409.
  • [13] Kim, J., Jung, Y. and Bang, H. (2018). Linear time-varying model predictive control of magnetically actuated satellites in elliptic orbits, Acta Astronautica 151(1): 791–804.
  • [14] Lasheen, A., Saad, M.S., Emara, H.M. and Elshafei, A.L. (2017). Continuous-time tube-based explicit model predictive control for collective pitching of wind turbine, Energy 118(1): 1222–1233.
  • [15] Li, Y., Wang, H. and Meng, X. (2019). Almost periodic synchronization of fuzzy celluar neural networks with time-varying delays via state-feedback and impulsive control, International Journal of Applied Mathematics and Computer Science 29(2): 337–349, DOI: 10.2478/amcs-2019-0025.
  • [16] Ligthart, J.A.J., Poksawat, P., Wang, L. and Nijmeijer, H. (2017). Experimentally validated model predictive controller for a hexacopter, IFAC-PapersOnLine 50(1): 4076–4081.
  • [17] Ławryńczuk, M. (2014). Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach, Springer, Cham.
  • [18] Maciejowski, J. (2002). Predictive Control with Constraints, Prentice Hall, Harlow.
  • [19] Mzyk, G. (2014). Combined Parametric-Nonparametric Identification of Block-Oriented Systems, Lecture Notes in Control and Information Sciences, Vol. 454, Springer Verlag, Berlin.
  • [20] Oliveira, G.H.C., da Rosa, A., Campello, R.J.G.B., Machado, J.B. and Amaral, W.C. (2011). An introduction to models based on Laguerre, Kautz and other related orthonormal functions. Part I: Linear and uncertain models, International Journal of Modelling, Identification and Control 14(1/2): 121–132.
  • [21] Oliveira, G.H.C., da Rosa, A., Campello, R.J.G.B., Machado, J.B. and Amaral, W.C. (2012). An introduction to models based on Laguerre, Kautz and other related orthonormal functions. Part II: Non-linear models, International Journal of Modelling, Identification and Control 16(1): 1–14.
  • [22] Pazera, M., Buciakowski, M. and Witczak, M. (2018). Robust multiple sensor fault-tolerant control for dynamic non-linear systems: Application to the aerodynamical twin-rotor system, International Journal of Applied Mathematics and Computer Science 28(2): 297–308, DOI: 10.2478/amcs-2018-0021.
  • [23] Richalet, J. and O’Donovan, D. (2009). Predictive Functional Control: Principles and Industrial Applications, Springer, London.
  • [24] Takács, G., Batista, G., Gulan, M. and Rohal’-Ilkiv, B. (2016). Embedded explicit model predictive vibration control, Mechatronics 36(1): 54–62.
  • [25] Tatjewski, P. (2007). Advanced Control of Industrial Processes, Structures and Algorithms, Springer, London.
  • [26] van Donkelaar, E.T., Bosgra, O.H. and Van den Hof, P.M.J. (1999). Model predictive control with generalized input parametrization, Proceedings of the European Control Conference, ECC 1999, Karlsruhe, Germany, pp. 443–454, paper F0599.
  • [27] Wahlberg, B. (1991). System identification using Laguerre models, IEEE Transactions on Automatic Control 36(5): 551–562.
  • [28] Wang, L. (2001). Continuous time model predictive control design using orthonormal functions, International Journal of Control 74(16): 1588–1600.
  • [29] Wang, L. (2004). Discrete model predictive controller design using Laguerre functions, Journal of Process Control 14(2): 131–142.
  • [30] Wang, Y. and Boyd, S. (2010). Fast model predictive control using online optimization, IEEE Transactions on Control Systems Technology 18(2): 267–278.
  • [31] Witkowska, A. and Śmierzchalski, R. (2018). Adaptive backstepping tracking control for an over-actuated DP marine vessel with inertia uncertainties, International Journal of Applied Mathematics and Computer Science 28(4): 679–693, DOI: 10.2478/amcs-2018-0052.
  • [32] Zheng, Y., Zhou, J., Xu, Y., Zhang, Y. and Qian, Z. (2017). A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions, ISA Transactions 68(1): 127–140.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d85224f6-e5b9-452f-8deb-8d6a35513fc5
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