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Intelligent Predictive Control with Locally Linear Based Model Identification and Evolutionary Programming Optimization with Application to Fossil Power Plants

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Book cover Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3480))

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Abstract

In this paper, an intelligent predictive control algorithm based on Locally Linear Model Tree (LOLIMOT) is implemented to control a fossil fuel power unit. The controller is a non-model based system that uses a LOLIMOT identifier to predict the response of the plant in a future time interval. An evolutionary programming (EP) approach optimizes the identifier-predicted outputs and determines input sequence in a time window. This intelligent system provides a predictive control of multi-input multi-output nonlinear systems with slow time variation.

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References

  1. Astrom, K.J., Wittenmark, B.: Adaptive Control. Addison-Wesley, Reading (1989)

    Google Scholar 

  2. Tolle, H., Ersu, E.: Neurocontrol; Learning Control Systems Inspired by Neural Architectures and Human Problem Solving. Lecture notes in control and identification sciences. Springer, Berlin (1992)

    Google Scholar 

  3. Saint-Donat, J., Bhat, N., McAvoy, T.J.: Neural-Net based model predictive control. Advanced in Intelligent Control. In: Harris, C.J. (ed.) Tailor and Francis, London (1994)

    Google Scholar 

  4. Nelles, O.: Nonlinear system identification: From classical approaches to neural networks and fuzzy models. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  5. Nelles, O.: Local linear model tree for on-line identification of time variant nonlinear dynamic systems. In: Proc. International Conference on Artificial Neural Networks (ICANN), Bochum, Germany, pp. 115–120 (1996)

    Google Scholar 

  6. Clarke, D.: Advanced in model-based predictive control. Oxford University Press, New York (1994)

    Google Scholar 

  7. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, Berlin (1998)

    Google Scholar 

  8. Garduno-ramirez, R., Lee, K.Y.: Wide range operation of a power unit via feedforward fuzzy control. IEEE Trans. Energy Conversion 15(4), 421–426 (2000)

    Article  Google Scholar 

  9. Astrom, K.J., Bell, R.D.: Dynamic models for boiler-turbine-alternator units: Data logs and paramater estimation for a 160 MW unit. In: Report LUTFD2/(TFRT-3192)/1-137/(1987), Department of automatic control. Lung Institute of Technology, Lung (1987)

    Google Scholar 

  10. Fogel, L.J.: The future of evolutionary programming. In: Proc. 24th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA (1991)

    Google Scholar 

  11. Lai, L.L.: Intelligent system application in power engineering: Evolutionary programming and neural networks. John Wiley & Sons Inc., New York (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Young Researchers Club, Islamic Azad University, Tehran, Iran

    Mahdi Jalili-Kharaajoo

Authors
  1. Mahdi Jalili-Kharaajoo
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W, T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, P.O. Box, I-06123, Perugia, Italy

    Antonio Laganà

  5. Institute of High Performance Computing, IHCP, 1 Science Park Road, 01-01 The Capricorn, Singapore Science Park II, 117528, Singapore

    Heow Pueh Lee

  6. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  7. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  8. OptimaNumerics Ltd, P.O. Box, Belfast, United Kingdom

    Chih Jeng Kenneth Tan

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© 2005 Springer-Verlag Berlin Heidelberg

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Jalili-Kharaajoo, M. (2005). Intelligent Predictive Control with Locally Linear Based Model Identification and Evolutionary Programming Optimization with Application to Fossil Power Plants. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424758_107

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  • DOI: https://doi.org/10.1007/11424758_107

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25860-5

  • Online ISBN: 978-3-540-32043-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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