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2015 | 25 | 4 | 833-847
Tytuł artykułu

Nonlinear state-space predictive control with on-line linearisation and state estimation

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.
Rocznik
Tom
25
Numer
4
Strony
833-847
Opis fizyczny
Daty
wydano
2015
otrzymano
2014-07-10
poprawiono
2015-01-24
Twórcy
  • Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
  • Arahal, M., Berenguel, M. and Camacho, E. (1998). Neural identification applied to predictive control of a solar plant, Control Engineering Practice 6(3): 333-344.
  • Bismor, D. (2015). Extension of LMS stability condition over a wide set of signals, International Journal of Adaptive Control and Signal Processing 29(5): 653-670, DOI: 10.1002/acs.2500.
  • Camacho, E. and Bordons, C. (1999). Model Predictive Control, Springer, London.
  • Colin, G., Chamaillard, Y., Bloch, G. and Corde, G. (2007). Neural control of fast nonlinear systems application to a turbocharged SI engine with VCT, IEEE Transactions on Neural Networks 18(4): 1101-1114.
  • de Oliveira, N. and Biegler, L. (1995). An extension of Newton-type algorithms for nonlinear process control, Automatica 52(2): 281-286.
  • Deng, J., Becerra, V.M. and Stobart, R. (2009). Input constraints handling in an MPC/feedback linearization scheme, International Journal of Applied Mathematics and Computer Science 19(2): 219-232, DOI: 10.2478/v10006-009-0018-2.
  • Doyle, F., Ogunnaike, B. and Pearson, R. (1995). Nonlinear model-based control using second-order Volterra models, Automatica 31(5): 697-714.
  • Ellis, M., Durand, H. and Christofides, P. (2014). A tutorial review of economic model predictive control methods, Journal of Process Control 24(8): 1156-1178.
  • Gonzalez, A., Adam, E. and Marchetti, J. (2008). Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis, Chemical Engineering and Processing 47(12): 2184-2194.
  • Kuure-Kinsey, M., Cutright, R. and Bequette, B. (2006). Computationally efficient neural predictive control based on a feedforward architecture, Industrial and Engineering Chemistry Research 45(25): 8575-8582.
  • Ławryńczuk, M. (2007). A family of model predictive control algorithms with artificial neural networks, International Journal of Applied Mathematics and Computer Science 17(2): 217-232, DOI: 10.2478/v10006-007-0020-5.
  • Ławryńczuk, M. (2014). Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach, Studies in Systems, Decision and Control, Vol. 3, Springer, Heidelberg.
  • Lee, J. and Ricker, N. (1994). Extended Kalman filter based nonlinear model predictive control, Industrial and Engineering Chemistry Research 33(6): 1530-1541.
  • Maciejowski, J. (2002). Predictive Control with Constraints, Prentice Hall, Harlow.
  • Maeder, U. and Morari, M. (2010). Offset-free reference tracking with model predictive control, Automatica 46(9): 1469-1476.
  • Mayne, D. (2014). Model predictive control: Recent developments and future promise, Automatica 50(12): 2967-2986.
  • Megías, D., Serrano, J. and Ghoumari, M.E. (1999). Extended linearised predictive control: Practical control algorithms for non-linear systems, Proceedings of the European Control Conference, ECC 1999, Karlsruhe, Germany, F883.
  • Mu, J., Rees, D. and Liu, G. (2005). Advanced controller design for aircraft gas turbine engines, Journal of Process Control 13(8): 1001-1015.
  • Muske, K. and Badgwell, T. (2002). Disturbance modeling for offset-free linear model predictive control, Journal of Process Control 12(5): 617-632.
  • Pannocchia, G. and Bemporad, A. (2007). Combined design of disturbance model and observer for offset-free model predictive control, IEEE Transactions on Automatic Control 52(6): 1048-1053.
  • Pannocchia, G. and Rawlings, J. (2003). Disturbance models for offset-free model predictive control, AIChE Journal 49(2): 426-437.
  • Patan, K. and Korbicz, J. (2012). Nonlinear model predictive control of a boiler unit: A fault tolerant control study, International Journal of Applied Mathematics and Computer Science 22(1): 225-237, DOI: 10.2478/v10006-012-0017-6.
  • Qin, S. and Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice 11(7): 733-764.
  • Rawlings, J. and Mayne, D. (2009). Model Predictive Control: Theory and Design, Nob Hill Publishing, Madison.
  • Simon, D. (2006). Optimal State Estimation: Kalman, H∞ and Nonlinear Approaches, John Wiley & Sons, Hoboken, NJ.
  • Tatjewski, P. (2007). Advanced Control of Industrial Processes, Structures and Algorithms, Springer, London.
  • Tatjewski, P. (2010). Supervisory predictive control and on-line set-point optimization, International Journal of Applied Mathematics and Computer Science 20(3): 483-495, DOI: 10.2478/v10006-010-0035-1.
  • Tatjewski, P. (2014). Disturbance modeling and state estimation for offset-free predictive control with state-space models, International Journal of Applied Mathematics and Computer Science 24(2): 313-323, DOI: 10.2478/amcs-2014-0023.
  • Tatjewski, P. and Ławryńczuk, M. (2006). Soft computing in model-based predictive control, International Journal of Applied Mathematics and Computer Science 16(1): 7-26.
  • Wang, L. (2007). Model Predictive Control System Design and Implementation Using MATLAB, Springer, London.
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Bibliografia
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