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A computationally efficient stable dual-mode type nonlinear predictive control algorithm

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper describes a computationally efficient (sub-optimal) nonlinear predictive control algorithm. The algorithm uses a modified dual-mode approach which guarantees closed-loop stability. In order to reduce the computational burden, instead of online nonlinear optimisation used in the classical dual-mode control scheme, a nonlinear model of the plant is linearised on-line and a quadratic programming problem is solved. Calculation of the terminal set and implementation steps of the algorithm are detailed, especially for input-output models, which are widely used in practice.
Rocznik
Strony
99--132
Opis fizyczny
Bibliogr. 45 poz., rys., wykr.
Twórcy
autor
  • Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
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  • CHISCI, L. and MOSCA, E. (1994) Stabilizing I-O receding horizon control of CARMA plants. IEEE Transactions on Automatic Control 39, 614-618.
  • CLARKE, D.W. and SCATTOLINI, R. (1991) Constrained receding-horizon predictive control. Proceedings IEE, Part D 138, 347-354.
  • CLARKE, D.W. and MOHTADI. C. (1989) Properties of generalized predictive control. Automatica 25, 859-875.
  • CLARKE, D.W., MOHTADI, C. and TUFFS, P.S. (1987) Generalized predictive control - I. The basic algorithm, II. Extensions and interpretations. Automatica 23, 137-160.
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  • DECLERCQ, F. and DE KEYSER, R. (1999) Suboptimal nonlinear predictive controllers. International Journal of Applied Mathematics and Computer Science 9, 129-148.
  • GARCIA, C.E. (1984) Quadratic dynamic matrix control of nonlinear processes: an application to a batch reactor process. Proceedings of AIChE National Meeting, San Francisco, USA.
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  • KALMAN, R.E. and BERTRAM, J.E. (1960) Control system analysis and design via the “second method” of Lyapunov: I. Continuous-time systems, II. Discrete-time systems. Journal of Basic Engineering Transactions ASMS 82, 371-400.
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  • LI, W.C. and BIEGLER, T. (1989) Multistep, Newton-type control strategies for constrained, nonlinear systems. Chemical Engineering Research and Design 67, 562-577.
  • ŁAWRYŃCZUK, M. (2007) A family of model predictive control algorithms with artificial neural networks. International Journal of Applied Mathematics and Computer Science 17, 217-232.
  • ŁAWRYŃCZUK, M. and TATJEWSKI, P. (2006) An efficient nonlinear predictive control algorithm with neural models and its application to a high-purity distillation process. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M., eds., Lecture Notes in Artificial Intelligence, 4029: The 8th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2006, Zakopane, Poland, Springer, Heidelberg, 76-85.
  • ŁAWRYŃCZUK, M. and TATJEWSKI, P. (2004) A stable dual-mode type nonlinear predictive control algorithm based on on-line linearisation and quadratic programming. Proceedings of the 10th International Conference on Methods and Models in Automation and Robotics, Międzyzdroje, Poland, 503-510.
  • MACIEJOWSKI, J.A. (2003) Predictive Control with Constraints. Prentice Hall, Harlow.
  • MAGNI, L., DE NICOLAO, G., MAGNANI, L. and SCATTOLINI, R. (2001) A stabilizing model-based predictive control algorithm for nonlinear systems. Automatica 37, 1351-1362.
  • MARUSAK, P. and TATJEWSKI, P. (2003) Stable, effective fuzzy DMC algorithms with on-line quadratic optimisation. Proceeding of the American Control Conference, Denver, USA, 3513-3518.
  • MAYNE, D.Q., RAWLINGS, J.B., RAO, C.V. and SCOKAERT, P.O.M. (2000) Constrained model predictive control: stability and optimality. Automatica 36, 789-814.
  • MAYNE, D.Q. and MICHALSKA, H. (1990) Receding horizon control of nonlinear systems. IEEE Transactions on Automatic Control 35, 814-824.
  • MEADOWS, E.S., HENSON, M.A., EATON, J.W. and RAWLINGS, J.B. (1995) Receding horizon control and discontinuous state feedback stabilization. International Journal of Control 62, 1217-1229.
  • MEADOWS, E.S., and RAWLINGS, J.B. (1993) Receding horizon control with an infinite horizon. Proceedings of the American Control Conference, San Francisco, USA, 2926-2930.
  • MICHALSKA, H. and MAYNE, D.Q. (1993) Robust receding horizon control of constrained nonlinear systems. IEEE Transactions on Automatic Control 38, 1623-1633.
  • MOLLOV, S., BABUŠKA, R., ABONYI, J. and VERBRUGGEN, H.B. (2004) Effective Optimization for Fuzzy Model Predictive Control. IEEE Transactions on Fuzzy Systems 12, 661-675.
  • MORARI, M. and LEE, J. (1999) Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667-682.
  • MUSKE, K.R. and RAWLINGS, J.B. (1993) Model predictive control with linear models. AIChE Journal 39, 262-287.
  • MUTHA, R.K., CLUETT, W.R. and PENLIDIS, A. (1997) Nonlinear model-based predictive control of nonaffine systems. Automatica 33, 907-913.
  • DE NICOLAO, G., MAGNI, L. and SCATTOLINI, R. (1998) Stabilizing receding-horizon control of non-linear time-varying systems. IEEE Transactions on Automatic Control 43, 1030-1036.
  • DE OLIVEIRA KOTHARE, S.L. and MORARI, M. (2000) Contractive model predictive control for constrained nonlinear systems. IEEE Transactions on Automatic Control 45, 1053-1071.
  • OLIVEIRA, N.M.C. and BIEGLER, L.T. (1995) An extension of Newton-type algorithms for nonlinear process control. Automatica 31, 281-286.
  • QIN, S.J. and BADGWELL, T.A. (2003) A survey of industrial model predictive control technology. Control Engineering Practice 11, 733-764.
  • RAWLINGS. J.B. and MUSKE, K.R. (1993) The stability of constrained receding horizon control. IEEE Transactions on Automatic Control 38, 1512-1516.
  • ROSSITER, J.A. (2003) Model-Based Predictive Control. CRC Press, Boca Raton.
  • ROUHANI, R. and MEHRA, R.K. (1982) Model algorithmic control (MAC); basic theoretical properties. Automatica 18, 401-441.
  • SCATTOLINI, R. and BITTANTI, S. (1990) On the choice of the horizon in long-range predictive control - some simple criteria. Automatica 26, 915-917.
  • SCOKAERT, P.O.M., MAYNE, D.Q. and RAWLINGS, J.B. (1999) Suboptimal model predictive control (feasibility implies stability). IEEE Transactions on Automatic Control 3, 648-654.
  • SCOKAERT, P.O.M. (1997) Infinite horizon generalized predictive control. International Journal of Control 66, 161-175.
  • SCOKAERT, P.O.M., and CLARKE, D.W. (1994) Stabilising properties of constrained predictive control. Proceedings IEE, Part D, 141, 295-304.
  • TATJEWSKI, P. (2007) Advanced Control of Industrial Processes, Structures and Algorithms. Springer, London.
  • TATJEWSKI, P. and ŁAWRYŃCZUK. M. (2006) Soft computing in model-based predictive control. International Journal of Applied Mathematics and Computer Science 16, 101-120.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0027-0009
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