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Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Model predictive control (MPC) algorithms brought increase of the control system performance in many applications thanks to relatively easily solving issues that are hard to solve without these algorithms. The paper is focused on investigating how to further improve the control system performance using a trajectory of parameters weighting predicted control errors in the performance function of the optimization problem. Different shapes of trajectories are proposed and their influence on control systems is tested. Additionally, experiments checking the influence of disturbances and of modeling uncertainty on control system performance are conducted. The case studies were done in control systems of three control plants: a linear non-minimumphase plant, a nonlinear polymerization reactor and a nonlinear thin film evaporator. Three types of MPC algorithms were used during research: linear DMC, nonlinear DMC with successive linearization (NDMC–SL), nonlinear DMC with nonlinear prediction and linearization (NDMC–NPL). Results of conducted experiments are presented in greater detail for the control system of the polymerization reactor, whereas for the other two control systems only the most interesting results are presented, for the sake of brevity. The experiments in the control system of the linear plant were done as preliminary experiments with the modified optimization problem. In the case of control system of the thin film evaporator the researched mechanisms were used in the control system of a MIMO plant showing possibilities of improving the control system performance.
Rocznik
Strony
325--363
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr., wzory
Twórcy
  • Warsaw University of Technology, Institute of Control and Computation Engineering, Nowowiejska 15/19, 00–665 Warsaw, Poland
  • Warsaw University of Technology, Institute of Control and Computation Engineering, Nowowiejska 15/19, 00–665 Warsaw, Poland
Bibliografia
  • [1] I. Z. Ahmetzyanov and D. N. Dem’yanov: Determination of the Overshoot Scalar Control Systems with Transfer Zero and Binomial Law of Poles Distribution, Indian Journal of Science and Technology, 10(1) (2017).
  • [2] A. A. Aly and F. A. Salem: A New Accurate Analytical Expression for Rise Time Intended for Mechatronics Systems Performance Evaluation and Validation, International Journal of Automation, Control and Intelligent Systems, 1(2) (2015), 51-60
  • [3] M. G. Ardakani, B. Olofsson, A. Robertsson, and R. Johansson: Real-Time Trajectory Generation using Model Predictive Control, Proceedings of 2015 IEEE Conference on Automation Science and Engineering (CASE): Automation for a Sustainable Future. IEEE–Institute of Electrical and Electronics Engineers Inc., (2015), 942-948.
  • [4] S. Daniar, M. Shiroei, and R. Aazami: Multivariable predictive control considering time delay for load-frequency control in multi-area power systems, Archives of Control Sciences, 26(4) (2016), 527-549.
  • [5] F. J. Doyle III, B. A. Ogunnaike, and R. K. Pearson: Nonlinear Model-based Control Using Second order Volterra Models, Automatica, 31(5) (1995), 697-714.
  • [6] V. Exadaktylos and C. J. Taylor: Multi–objective performance optimisation for model predictive control by goal attainment, International Journal of Control, 83(7) (2010), 1374-1386.
  • [7] C. E. Garcia and A. M. Morshedi: Quadratic Programming Solution of Dynamic Matrix Control (QDMC), Chemical Engineering Communications, 46(1-3) (1986), 73-87.
  • [8] P. Marusak: Predictive controllers with presumed trajectory of control changes and efficient mechanism of output constraints handling (in Polish), Pomiary Automatyka Robotyka, 2/2008, 581-590.
  • [9] G. A. Nery Júnior, M. A. F. Martins, and R. Kalid: A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty, ISA Transactions, 53(2) (2014), 560-567
  • [10] M. F. Nor Shah, M. A. Zainal, A. Faruq, and S. S. Abdullah: Metamodeling Approach for PID Controller Optimization in an Evaporator Process, Proceedings of Fourth International Conference on Modeling, Simulation and Applied Optimization, 19–21 April 2011, Kuala Lumpur, Malaysia (2011).
  • [11] C. Rowe and J. Maciejowski: Tuning MPC using Hꚙ Loop Shaping, In Proceedings of the 2000 American Control Conference, 2 (2000), 1332-1336.
  • [12] C. Rowe and J. M. Maciejowski: Tuning Robust Model Predictive Controllers using LQG/LTR, In Proceedings of the 14th Triennial World Congress, 32(2), (1999), 1231-1236.
  • [13] D. E. Seborg, T. F. Edgar, D. A. Mellichamp, and F.J. Doyle III: Process dynamics and control, John Wiley & Sons, 2011.
  • [14] R. Shridhar and D. J. Cooper: A tuning strategy for unconstrained SISO model predictive control. Industrial and Engineering Chemistry Research, 36(3) (1997), 729-746.
  • [15] R. Shridhar and D. J . Cooper: A tuning strategy for unconstrained multi-variable model predictive control, Industrial and Engineering Chemistry Research, 37(10) (1998), 4003-4016.
  • [16] M. A. Stephens, C. Manzie, and M. C. Good: Model Predictive Control for Reference Tracking on an Industrial Machine Tool Servo Drive, IEEE Transactions on Industrial Informatics, 9(2) (2013), 808-816.
  • [17] P. Tatjewski: Advanced Control of Industrial Processes. Structures and Algorithms, Springer-Verlag, London, 2007.
  • [18] P. Tatjewski: Offset-free nonlinear Model Predictive Control with state-space process models, Archives of Control Sciences, 27(4), (2017), 595-615.
  • [19] J. O. Trierweiler and L. A. Farina: RPN Tuning Strategy for Model Predictive Control, Journal of Process Control, 13(7) (2003), 591-598.
  • [20] A. S. Yamashita, P. M. Alexandre, A. C. Zanin, and D. Odloak: Reference trajectory tuning of model predictive control, Control Engineering Practice, 50 (2016), 1-11.
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-760c06aa-1875-45bf-b109-be44e66286a3
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