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An Efficient Nonlinear Predictive Control Algorithm with Neural Models and Its Application to a High-Purity Distillation Process

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

This paper is concerned with a computationally efficient (suboptimal) nonlinear model-based predictive control (MPC) algorithm and its application to a high-purity high-pressure ethylene-ethane distillation column. A neural model of the process is used on-line to determine the local linearisation and the nonlinear free response. In comparison with general nonlinear MPC technique, which hinges on non-convex optimisation, the presented structure is far more reliable and less computationally demanding because it results in a quadratic programming problem, whereas its closed-loop control performance is similar.

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References

  1. Haykin, S.: Neural networks – a comprehensive foundation. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  2. Henson, M.A.: Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering 23, 187–202 (1998)

    Article  Google Scholar 

  3. Liu, G.P., Kadirkamanathan, V., Billings, S.A.: Predictive control for non-linear systems using neural networks. International Journal of Control 71, 1119–1132 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  4. Ławryńczuk, M., Tatjewski, P.: A stable dual-mode type nonlinear predictive control algorithm based on on-line linearisation and quadratic programming. In: Proceedings of the 10th International Conference on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, pp. 503–510 (2004)

    Google Scholar 

  5. Ławryńczuk, M.: Nonlinear model predictive control algorithms with neural models of processes (in Polish). PhD thesis. Warsaw University of Technology (2003)

    Google Scholar 

  6. Ławryńczuk, M., Tatjewski, P.: A computationally efficient nonlinear predictive control algorithm based on neural models. In: Proceedings of the 8th International Conference on Methods and Models in Automation and Robotics, Szczecin, Poland, pp. 781–786 (2002)

    Google Scholar 

  7. Maciejowski, J.M.: Predictive control with constraints. Prentice-Hall, Englewood Cliffs (2002)

    Google Scholar 

  8. Mahfouf, M., Linkens, D.A.: Non-linear generalized predictive control (NLGPC) applied to muscle relaxant anaesthesia. International Journal of Control 71, 239–257 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  9. Michalska, H., Mayne, D.Q.: Robust receding horizon control of constrained nonlinear systems. IEEE Transactions on Automatic Control 38, 1623–1633 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  10. Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667–682 (1999)

    Article  Google Scholar 

  11. Parisini, T., Sanguineti, M., Zoppoli, R.: Nonlinear stabilization by receding-horizon neural regulators. International Journal of Control 70, 341–362 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  12. Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M.: Nonlinear model predictive control using neural networks. IEEE Control Systems Magazine 20, 56–62 (2000)

    Article  Google Scholar 

  13. Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)

    Article  Google Scholar 

  14. Tatjewski, P., Ławryńczuk, M.: Soft computing in model-based predictive control. International Journal of Applied Mathematics and Computer Science 16, 101–120 (2006)

    MathSciNet  Google Scholar 

  15. Tatjewski, P.: Advanced Control of Industrial Processes. Structures and Algorithms (in Polish). EXIT Academic Publishing House. Warsaw (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Maciej Ławryńczuk & Piotr Tatjewski

Authors
  1. Maciej Ławryńczuk
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  2. Piotr Tatjewski
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Ławryńczuk, M., 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) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_9

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  • DOI: https://doi.org/10.1007/11785231_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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