Skip to main content

On the efficient computation of higher order maps ad kf g(x) using Taylor arithmetic and the Campbell-Baker-Hausdorff formula

  • Conference paper
  • First Online:
Nonlinear and Adaptive Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 281))

Abstract

The computation of ad kf g requires derivatives of f and g up to order k. For small dimensions, the Lie brackets can be computed with computer algebra packages. The application to non-trivial systems is limited due to a burden of symbolic computations involved. The author proposes a method to compute function values of ad kf g using automatic differentiation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. Bendtsen and O. Stauning. TADIFF, a flexible C++ package for automatic differentiation. Technical Report IMM-REP-1997-07, TU of Denmark, Dept. of Mathematical Modelling, Lungby, 1997.

    Google Scholar 

  2. A. Bensoussan and J. L. Lions, editors. Analysis and Optimization of Systems, Part 2, volume 63 of Lecture Notes in Control and Information Science. Springer, 1984.

    Google Scholar 

  3. Y. F. Chang. Automatic solution of differential equations. In D. L. Colton and R. P. Gilbert, editors, Constructive and Computational Methods for Differential and Integral Equations, volume 430 of Lecture Notes in Mathematics, pp. 61–94. Springer Verlag, New York, 1974.

    Chapter  Google Scholar 

  4. Y. F. Chang. The ATOMCC toolbox. BYTE, 11(4):215–224, 1986.

    Google Scholar 

  5. B. Christianson. Reverse accumulation and accurate rounding error estimates for Taylor series. Optimization Methods and Software, 1:81–94, 1992.

    Article  Google Scholar 

  6. G. F. Corliss and Y. F. Chang. Solving ordinary differential equations using Taylor series. ACM Trans. Math. Software, 8:114–144, 1982.

    Article  MATH  MathSciNet  Google Scholar 

  7. J.-M. Cornil and P. Testud. An Introduction to Maple V. Springer, 2001.

    Google Scholar 

  8. B. de Jager. The use of symbolic computation in nonlinear control: is it viable? IEEE Trans. on Automatic Control, AC-40(1):84–89, 1995.

    Article  MathSciNet  Google Scholar 

  9. A. Griewank. ODE solving via automatic differentiation and rational prediction. In D. F. Griffiths and G. A. Watson, editors, Numerical Analysis 1995, volume 344 of Pitman Research Notes in Mathematics Series. Addison-Wesley, 1995.

    Google Scholar 

  10. A. Griewank. Evaluating Derivatives — Principles and Techniques of Algorithmic Differentiation, volume 19 of Frontiers in Applied Mathematics. SIAM, 2000.

    Google Scholar 

  11. A. Griewank, D. Juedes, and J. Utke. A package for automatic differentiation of algorithms written in C/C++. ACM Trans. Math. Software, 22:131–167, 1996.

    Article  MATH  Google Scholar 

  12. R. Hermann and A. J. Krener. Nonlinear controllability and observability. IEEE Trans. on Automatic Control, AC-22(5):728–740, 1977.

    Article  MathSciNet  Google Scholar 

  13. A. Isidori. Nonlinear Control Systems: An Introduction. Springer, 3rd edition, 1995.

    Google Scholar 

  14. B. Jakubczyk, W. Respondek, and K. Tchon, editors. Geomatric Theory of Nonlinear Control Systems. Technical University of Wroclaw, 1985.

    Google Scholar 

  15. D. Juedes and K. Balakrishnan. Generalized neuronal networks, computational differentiation, and evolution. In M. Berz, editor, Proc. of the Second International Workshop, pp. 273–285. SIAM, 1996.

    Google Scholar 

  16. A. J. Krener. (ad f,g), (ad f,g) and locally (ad f,g) invariant and controllability distributions. SIAM J. Control and Optimization, 23(4):523–524, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  17. A. J. Krener and A. Isidori. Linearization by output injection and nonlinear observers. Systems & Control Letters, 3:47–52, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  18. A. Kugi, K. Schlacher, and R. Novaki. Symbolic Computation for the Analysis and Sythesis of Nonlinear Control Systems, volume IV of Software for Electrical Engineering, Analysis and Design, pp. 255–264. WIT-Press, 1999.

    Google Scholar 

  19. R. Marino and G. Cesareo. Nonlinear control theory and symbolic algebraic manipulation. In Mathematical Theory of Networks and Systems, Proc. of MTNS’83, Beer Sheva, Israel, June 20–24, 1983, volume 58 of Lecture Notes in Control and Information Science, pp. 725–740. Springer, 1984.

    Google Scholar 

  20. R. Marino and G. Cesareo. The use of symbolic computation for power system stabilization: An example of computer aided design. In J. L. Lions, editors. Analysis and Optimization of Systems, Part 2, volume 63 of Lecture Notes in Control and Information Science. Springer, 1984 Bensoussan and Lions [2], pp. 598–611.

    Google Scholar 

  21. Neil Munro, editor. Symbolic methods in control system analysis and design. IEE, 1999.

    Google Scholar 

  22. H. Nijmeijer and A. J. van der Schaft. Nonlinear Dynamical Control systems. Springer, 1990.

    Google Scholar 

  23. W. Oevel, F. Postel, G. Rüscher, and St. Wehrmeier. Das MuPAD Tutorium. Springer, 1999. Deutsche Ausgabe.

    Google Scholar 

  24. K. Röbenack and K. J. Reinschke. A efficient method to compute Lie derivatives and the observability matrix for nonlinear systems. In Proc. 2000 International Symposium on Nonlinear Theory and its Applications (NOLTA’2000), Dresden, Sept. 17–21, volume 2, pp. 625–628, 2000.

    Google Scholar 

  25. K. Röbenack and K. J. Reinschke. Reglerentwurf mit Hilfe des Automatischen Differenzierens. Automatisierungstechnik, 48(2):60–66, 2000.

    Article  Google Scholar 

  26. R. Rothfuß, J. Schaffner, and M. Schaffner, and M. Zeitz. Rechnergestützte Analyse und Synthese nichtlinearer Systeme. In S. Engell, editor, Nichtlineare Regelungen: Methoden, Werkzeuge, Anwendungen, volume 1026 of VDI-Berichte, p. 267–291. VDI-Verlag, Düsseldorf, 1993.

    Google Scholar 

  27. E. D. Sontag. Mathematical Control Theory, volume 6 of Texts in Applied Mathematics. Springer-Verlag, 2nd edition, 1998.

    Google Scholar 

  28. S. Wolfram. The MATHEMATICA Book. Cambridge University Press, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Röbenack, K. (2003). On the efficient computation of higher order maps ad kf g(x) using Taylor arithmetic and the Campbell-Baker-Hausdorff formula. In: Zinober, A., Owens, D. (eds) Nonlinear and Adaptive Control. Lecture Notes in Control and Information Sciences, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45802-6_26

Download citation

  • DOI: https://doi.org/10.1007/3-540-45802-6_26

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43240-1

  • Online ISBN: 978-3-540-45802-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics