Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2007 | 17 | 2 | 217-232

Tytuł artykułu

A family of model predictive control algorithms with artificial neural networks

Treść / Zawartość

Warianty tytułu

Języki publikacji



This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.








Opis fizyczny




  • Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00–665 Warsaw, Poland


  • Kesson B. M. and Toivonen H. T. (2006): A neural network model predictive controller. - J. Process Contr., Vol.16, No.3, pp.937-946.
  • Bacic M., Cannon M. and Kouvaritakis B. (2002): Feedback linearization MPC for discrete-time bilinear systems. - Proc. 15-th IFAC World Congress, Barcelona, Spain, CD-ROM, paper 2391.
  • Babuška R., Sousa J. M. and Verbruggen H. B.(1999): Predictive control of nonlinear systems based on fuzzy and neural models. - Proc. European Control Conf., Karlsruhe, Germany, CD-ROM, paper F1032-5.
  • Bazaraa M. S., Sherali J. and Shetty K. (1993): Nonlinear Programming: Theory and Algorithms. - New York: Wiley.
  • Bloemen H.H.J., van den Boom T. J. J. and Verbruggen H. B. (2001): Model-based predictive control for Hammerstein-Wiener systems. - Int. J. Contr., Vol.74, No.5, pp.482-495.
  • Brdyś M.A. and Tatjewski P. (2005): Iterative algorithms for multilayer optimizing control. - London: Imperial CollegePress/World Scientific.
  • Cavagnari L., Magni L. and Scattolini R. (1999): Neural network implementation of nonlinear receding-horizon control.- Neural Comput. Applic., Vol.8, No.1, pp.86-92.
  • Clarke D. W., Mohtadi C. and Tuffs P. S. (1987): Generalized predictive control - I. The basic algorithm. - Automatica, Vol.23, No.2, pp.137-148.
  • Cutler R. and Ramaker B. (1979): Dynamic matrix control - A computer control algorithm. - Proc. AIChE National Meeting, Houston.
  • Dutka A. and Ordys A. W. (2004): The optimalnon-linear generalised predictive control by the time-varying approximation.- Proc. 10-th IEEE Int. Conf. Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp.299-303.
  • Grimble M.J. and Ordys A.W. (2001): Nonlinear predictive control for manufacturing and robotic applications. - Proc. 7-th IEEE Int. Conf. Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp.579-592.
  • Haykin S. (1999): Neural Networks - A Comprehensive Foundation. - Englewood Cliffs, NJ: Prentice Hall.
  • Henson M. A. (1998): Nonlinear model predictive control: Current status and future directions. - Comput. Chemi. Engi., Vol.23, No.2, pp.187-202.
  • Hornik K., Stinchcombe M. and White H. (1989): Multilayer feed forward networks are universal approximators. - Neural Netw., Vol.2, No.5, pp.359-366.
  • Hussain M. A. (1999): Review of theapplications of neural networks in chemical process control - Simulation andonline implementation. - Artifi. Intelli. Eng., Vol.13, No.1, pp.55-68.
  • Kavsek B.K., Skrjanc I. and Matko D. (1997): Fuzzy predictive control of a highly nonlinear pH process. - Comput. Chem. Eng., Vol.21, Supplement, pp.S613-S618.
  • Kouvaritakis B., Cannon M. and Rosser J. A.(1999): Nonlinear model based predictive control. - Int.J. Contr., Vol.72, No.10, pp.919-928.
  • Liu G. P., Kadirkamanathan V. and Billings S. A. (1998): Predictive control for non-linear systems using neural networks. - Int. J. Contr., Vol.71, No.6, pp.1119-1132.
  • Ławryńczuk M. and Tatjewski P. (2006): An efficient nonlinear predictive control algorithm with neural models and its application to a high-purity distillation process. - Lecture Notes in Artificial Intelligence, Springer, Vol.4029, pp.76-85.
  • Ławryńczuk M. and Tatjewski P. (2004): A stable dual-mode type nonlinear predictive control algorithm basedon on-line linearisation and quadratic programming. - Proc.10-th IEEE Int. Conf. Methods and Models in Automationand Robotics, Międzyzdroje, Poland, pp.503-510.
  • 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. (2003): An iterative nonlinear predictive control algorithm based on linearisation and neural models. - Proc. European Control Conf., Cambridge, U.K., CD-ROM, paper 339.
  • Ławryńczuk M. and Tatjewski P. (2002): A computationally efficient nonlinear predictive control algorithm based on neural models. - Proc. 8-th IEEE Int. Conf. Methods and Models in Automation and Robotics, Szczecin, Poland, pp.781-786.
  • Ławryńczuk M. and Tatjewski P. (2001): A multivariable neural predictive control algorithm. - Proc. IFAC Advanced Fuzzy-Neural Control Workshop, Valencia, Spain, pp.191-196.
  • Maciejowski J.M. (2002): Predictive Control with Constraints. - Harlow, U.K.: Prentice Hall.
  • Mahfouf M. and Linkens D.A. (1998): Non-linear generalized predictive control (NLGPC) applied to muscle relaxant anaesthesia. - Int. J. Contr., Vol.71, No.2, pp.239-257.
  • Maner B.R., Doyle F.J., Ogunnaike B.A. and Pearson R.K. (1996): Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models. - Automatica, Vol.32, No.9, pp.1285-1301.
  • Michalska H. and Mayne D.Q. (1993): Robust receding horizon control of constrained nonlinear systems. - IEEE Trans. Automat. Cont., Vol.38, No.11, pp.1623-1633.
  • Morari M. and Lee J. (1999): Model predictive control: Past, present and future. - Comput. Chem. Engi.,Vol.23, No.4/5, pp.667-682.
  • Nørgaard M., Ravn O., Poulsen N. K. and Hansen L.K. (2000): Neural Networks for Modelling and Control of Dynamic Systems. - London: Springer.
  • Osowski S. (1996): Neural Networks - An Algorithmic Approach. - Warsaw, Poland: WNT.
  • 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.
  • 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. (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.
  • Sriniwas G. R. and Arkun Y.(1997): A global solution to the non-linear model predictive control algorithms using polynomial ARX models. - Comput. Chem. Engi., Vol.21, No.4, pp.431-439.
  • Tatjewski P. (2007): Advanced Control of Industrial Processes, Structures and Algorithms. - London: Springer.
  • Tatjewski P. and Ławryńczuk M. (2006): Soft computing in model-based predictive control. - Int.J. Appl. Math. Comput. Sci., Vol.16, No.1, pp.101-120.
  • Trajanoski Z. and Wach P. (1998): Neural predictive control for insulin delivery using the subcutaneous route. - IEEE Trans. Biomed. Eng., Vol.45, No.9, pp.1122-1134.
  • 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.
  • Zheng A. (1997): A computationally efficient nonlinear MPC algorithm. - Proc. American Control Conf., Albuquerque, pp.1623-1627

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.