Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5226))

Included in the following conference series:

Abstract

By combining dynamical feature of objects with requirements of control performance, predictive model uses a second order slow time varying linear model to characterize original controlled station. This paper introduces a way to establish a category of predictive models of nonlinear objects and lead this kind of model into predictive functional control (PFC) so then obtain nonlinear PFC arithmetic based on characteristic model. We successfully avoid the problem of online identification of slow time varying parameters by setting parameter interval, thereby this kind of control arithmetic is typical of easy operation, high speed and wide applicability in comparison with traditional PFC. Finally this paper offers applied living examples by considering temperature control problem of continuous stirring tank reactor(CSTR).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Wu, H.X.: Intelligent Characteristic Model and Intelligent Control. Acta automatica sinica 28 Supp.1, 30–37 (2002)

    Google Scholar 

  2. Wu, H.X., Xie, Y.C., Li, Z.B., He, Y.Z.: Intelligent Control based on Description of Plant Characteristic Model. Acta automatica sinica 25(1), 9–17 (1999)

    Google Scholar 

  3. Wu, H.X., Wang, Y.C., Xing, Y.: Intelligent Control based on Intelligent Characteristic Model and its Application. SCIENCE IN CHINA (Series E) 32(6), 805–816 (2002)

    Google Scholar 

  4. Wu, H.X., Wang, Y., Xie, Y.C.: Nonlinear Golden Section Adaptive Control. Journal of Astronautics 23(6), 1–8 (2002)

    Google Scholar 

  5. Wu, H.X., Wang, Y., Xie, Y.C.: Characteristic Modeling and Control Method of Nonlinear System. Control Enginerring 6, 1–7 (2002)

    Google Scholar 

  6. Guo, J., Chen, Q.W., Zhu, R.J., He, W.L.: Adaptive Predictive Control of a Class of Nonlinear System. Control theory and Applications 19(1), 68–72 (2002)

    MATH  MathSciNet  Google Scholar 

  7. Richalet, J., Doss, S.A., Arber, C., et al.: Predictive Functional Control: Applications to Fast and Accurate Robots. In: Isermann, R. (ed.) Automatic Control 10th Triennial World Congress of IFAC, pp. 251–258. Pergamon Press, Oxford (1988)

    Google Scholar 

  8. Richalet, J., Rault, A., Testud, J.L., Papon, J.: Model Predictive Heuristic Control: Application to Industrial Processes. Automatics 14(5), 413–428 (1978)

    Article  Google Scholar 

  9. Wu, J.G., Zhang, P.J.: Study of Pure Time Delay System based on Predictive Functional Control. Process automation instrumentation 25(11), 8–10 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, P., Wu, J., Fei, M. (2008). The Research and Application of Nonlinear Predictive Functional Control Based on Characteristic Models. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_95

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87442-3_95

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87440-9

  • Online ISBN: 978-3-540-87442-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics