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2006 | 16 | 1 | 7-26
Tytuł artykułu

Soft computing in modelbased predictive control footnotemark

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.
Rocznik
Tom
16
Numer
1
Strony
7-26
Opis fizyczny
Daty
wydano
2006
otrzymano
2005-11-18
poprawiono
2005-12-21
Twórcy
  • Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
  • 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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  • Ławryńczuk M. (2003): Nonlinear model predictive control algorithms with neural models. — Ph.D. thesis, Warsaw University of Technology, Warsaw, Poland.
  • Ławryńczuk M. and Tatjewski P. (2001a): A multivariable neural predictive control algorithm. — Proc. IFAC Advanced Fuzzy-Neural Control Workshop, Valencia, Spain, pp. 191–196.
  • Ławryńczuk M. and Tatjewski P. (2001b): A nonlinear predictive control algorithm for processes modelled by means of neural networks. — Proc. 7-th Int. Conf. Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp. 489–494.
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  • Ławryńczuk M. and Tatjewski P. (2004): A stable dual-mode type nonlinear predictive control algorithm based on online linearisation and quadratic programming. — Proc. 10-th Int. Conf. Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp. 503–510.
  • Maciejowski J.M. (2002): Predictive Control. — Harlow: Prentice Hall.
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  • Parisini T., Sanguineti M. and Zoppoli R. (1998): Nonlinear stabilization by receding-horizon neural regulators. — Int. J. Contr., Vol. 70, No. 3, pp. 341–362.
  • Parisini T. and Zoppoli R. (1995): A receding-horizon regulator for nonlinear systems and a neural approximation. — Automatica, Vol. 31, No. 10, pp. 1443–1451.
  • Piche S., Sayyar-Rodsari B., Johnson D. and Gerules M. (2000): Nonlinear model predictive control using neural networks. — IEEE Contr. Syst. Mag., Vol. 20, No. 3, pp. 56–62.
  • Qin S.J. and Badgwell T.A. (2003): A survey of industrial model predictive control technology. — Contr. Eng. Pract., Vol. 11, No. 7, pp. 733–764.
  • Rossiter J.A. (2003): Model-Based Predictive Control. — Boca Raton, FL: CRC Press.
  • Tatjewski P. (2002): Advanced Control of Industrial Processes. Structures and Algorithms. — Warsaw: Akademicka Oficyna Wydawnicza EXIT, (in Polish, revised and extended English edition in preparation).
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  • Wang L.X. and Wan F. (2001): Structured neural networks for constrained model predictive control. — Automatica, Vol. 37, No. 8, pp. 1235–1243.
  • Yu D.L. and Gomm J.B. (2003): Implementation of neural network predictive control to a multivariable chemical reactor. — Contr. Eng. Pract., Vol. 11, No. 11, pp. 1315–1323.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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