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2006 | 16 | 1 | 7-26

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Soft computing in modelbased predictive control footnotemark

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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.








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  • 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


  • Allgöwer F., Badgwell T.A., Qin J.S., Rawlings J.B. and Wright S.J. (1999): Nonlinear predictive control and moving horizon estimation – An introductory overview, In: Advances in Control – Highlights of ECC’99 (P.M. Frank, ed.).— London: Springer, pp. 391–449.
  • 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, (on CD-ROM).
  • Bazaraa M.S., Sherali J. and Shetty K. (1993): Nonlinear Programming: Theory and Algorithms. — New York: Wiley.
  • Bemporad A., Morari M, Dua V. and Pistikopoulos E.N. (2002): The explicit linear quadratic regulator for constrained systems. — Automatica, Vol. 38, No. 1, pp. 3–20.
  • Brdyś M.A. and Tatjewski P. (2005): Iterative Algorithms for Multilayer Optimizing Control. — London: Imperial College Press/World Scientific.
  • Camacho E.F. and Bordons C. (1999): Model Predictive Control. — London: Springer.
  • 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.L. (1979): Dynamic matrix control – A computer control algorithm. — Proc. AIChE National Meeting, Houston.
  • Eder H.H. (1999): MBPC benefits and key success factors. — Proc. 5-th European Control Conf., ECC’99, Karlsruhe, Germany, (on CD-ROM).
  • Findeisen W., Bailey F.N., Brdys M., Malinowski K., Tatjewski P. and Wozniak A. (1980): Control and Coordination in Hierarchical Systems. — Chichester: Wiley.
  • Findeisen W. (1997): Control Structures for Complex Processes. — Warsaw: Warsaw University of Technology Press, (in Polish).
  • Garcia C.E. (1984): Quadratic/dynamic matrix control of non- linear processes: An application to a batch reaction process. — Proc. AIChE Annual Meeting, San Francisco.
  • Haimovich H., Seron. M.M., Goodwin G.C. and Agüero J.C. (2003): A neural approximation to the explicit solution of constrained linear MPC. — Proc. European Control Conf., Cambridge, UK, (on CD-ROM).
  • Haykin S. (1999): Neural Networks – A Comprehensive Foundation. — Englewood Cliffs, NY: Prentice Hall.
  • Henson M.A. (1998): Nonlinear model predictive control: Current status and future directions. — Comput. Chem. Eng., Vol. 23, No. 2, pp. 187–202.
  • Hoekstra P., van den Boom T.J.J. and Botto M.A. (2001): Design of an analytic constrained predictive controller using neural networks. — Proc. European Control Conf., Porto, Portugal, (on CD-ROM).
  • Johansen T.A. (2004): Approximate explicit receding horizon control of constrained nonlinear systems. — Automatica, Vol. 40, No. 2, pp. 293–300.
  • Liu G.P. and Daley S. (1999): Output-model-based predictive control of unstable combustion systems using neural networks. — Contr. Eng. Pract., Vol. 7, No. 5, pp. 591–600.
  • 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. (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.
  • Ławryńczuk M. and Tatjewski P. (2002): A computationally efficient nonlinear predictive control algorithm based on neural models. — Proc. 8-th Int. Conf. Methods and Models in Automation and Robotics, Szczecin, Poland, pp. 781–786.
  • 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.
  • Marusak P. and Tatjewski P. (2002): Stability analysis of non- linear control systems with unconstrained fuzzy predictive controllers. — Arch. Contr. Sci., Vol. 12, pp. 267–288.
  • Mayne D.Q., Rawlings J.B., Rao C.V. and Scokaert P.O.M. (2000): Constrained model predictive control: Stability and optimality. — Automatica, Vol. 36, No. 6, pp. 789– 814.
  • Michalska H. and Mayne D.Q. (1993) (2000): Robust receding horizon control of constrained nonlinear systems. — IEEE Trans. Automat. Contr., Vol. 38, No. 11, pp. 1623–1633.
  • Morari M. and Lee J.H. (1999): Model predictive control: Past, present and future. — Comput. Chem. Eng., Vol. 23, No. 4/5, pp. 667–682.
  • Najim K., Rusnak A., Meszaros A. and Fikar M. (1997): Constrained long-range predictive control based on artificial neural networks. — Int. J. Syst. Sci., Vol. 28, No. 12, pp. 1211–1226.
  • Nørgaard M., Ravn O., Poulsen N.K. and Hansen L.K. (2000): Neural Networks for Modelling and Control of Dynamic Systems. — London: Springer.
  • Ortega J.G. and Camacho E.F. (1996): Mobile robot navigation in a partially structured static environment, using neural predictive control. — Contr. Eng. Pract., Vol. 4, No. 12, pp. 1669–1679.
  • Parisini T. and Sacone S. (2001): Stable hybrid control based on discrete-event automata and receding-horizon neural regulators. — Automatica, Vol. 37, No. 8, pp. 1279–1292.
  • 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).
  • Temeng K.O., Schnelle P.D. and McAvoy T.J. (1995): Model predictive control of an industrial packed bed reactor using neural networks. — J. Process Contr., Vol. 5, No. 1, pp. 19– 27.
  • 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.
  • Vila J.P. and Wagner V. (2003): Predictive neuro-control of uncertain systems: Design and use of a neuro-optimizer. — Automatica, Vol. 39, No. 5, pp. 767–777.
  • 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.

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