Skip to main content
Log in

A Kolmogorov-Fokker-Planck approach for a stochastic Duffing-van der Pol system

  • Published:
Differential Equations and Dynamical Systems Aims and scope Submit manuscript

Abstract

The stochastic Duffing-van der Pol (SDvdP) system is an appealing case in stochastic system theory, since it involves linear and non-linear vector fields, i.e. system non-linearities, state-independent and state-dependent stochastic accelerations. The equation describing the Duffing-van der Pol system is a second-order non-linear autonomous differential equation. Stochastic estimation procedures, without accounting for possible small stochastic accelerations, may cause an inaccurate estimation of positioning of dynamical systems. In this paper, the estimation-theoretic scenarios of the stochastic version of the Duffing-van der Pol system, which accounts for a state-independent perturbation as well as a state-dependent stochastic perturbation of the order n, where n ≥ 1, is the subject of investigation. This paper discusses the notion of a qualitative analysis for the non-linear stochastic differential system using the Itô differential rule, and subsequently the method is applied to the stochastic system of this paper. This paper develops and explores the efficacy of three different approximate estimation procedures for analyzing the non-linear problem of concern here as well. The theory of the estimation procedures of this paper is developed using the Kolmogorov-Fokker-Planck equation. Numerical simulations involving two different sets of initial conditions are given, since approximate estimation procedures are hard to evaluate theoretically. This paper suggests that the higher-order approximate estimation procedure will lead to the more accurate state estimates.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. V.I. Arnold, “Ordinary Differential Equations”, The MIT Press, Cambridge and Massachusetts, 1995.

    Google Scholar 

  2. L. Arnold, N. Sri Namachchivaya and K.R. Schenk-Hoppé, Toward an understanding of stochastic Hopf bifurcation: a case study, Int.J.Bif. Chaos, 6(1996), 1947–1975.

    Article  Google Scholar 

  3. P.H. Baxendale and L. Goukasian, Lyapunov exponents for small random perturbations of Hamiltonian systems, The Ann. Prob., 30(2002),101–134.

    Article  MATH  MathSciNet  Google Scholar 

  4. M.M. Bierbaum, R.I. Joseph, R.L. Fry and J.B. Nelson, A Fokker-Planck model for a two-body problem, AIP Conf. Proc., 617(2002), 340–371.

    Article  MathSciNet  Google Scholar 

  5. S. Challa and Y. Bar-Salom, Non-linear filter design using Kolmogorov-Fokker-Planck probability density evolutions, IEEE Tran. Aerosp. and Elect. Sys., 36(2000), 309–314.

    Article  Google Scholar 

  6. F. Carravetta, A. Germani and M. K Shuakayev, A new sub optimal approach to the filtering problem for bilinear stochastic differential systems, SIAM J. Contr. Opt. 38(2000), 1171–1203.

    Article  MATH  MathSciNet  Google Scholar 

  7. R. L. Chang, Pre-computed-gain non-linear filters for non-linear systems with state-dependent noise, J. of Dyn. Syst., Meas. and Contr., 112(1990), 270–275.

    Article  MATH  Google Scholar 

  8. D. Dacunha-Castelle and D. Florens-Zmirou, Estimations of the coefficients of a diffusion from discrete observations, Stochastics, 19(1986), 263–284.

    MATH  MathSciNet  Google Scholar 

  9. W. Feller, “An Introduction to Probability Theory and its Applications”, John Wiley and Sons, New York, 2000.

    Google Scholar 

  10. A. Germani, C. Manes and P. Palumbo, Filtering of stochastic nonlinear differential systems via a Carleman approximation approach, IEEE Trans. Autom. Cont., 52(2007), 2166–2172.

    Article  MathSciNet  Google Scholar 

  11. A. H. Jazwinski, “Stochastic Processes and Filtering Theory”, Academic Press, New York and London, 1970.

    MATH  Google Scholar 

  12. S. J. Julier, J. K. Uhalmann and H. F. Durrant-Whyte, A new approach for filtering non-linear systems, American Contr. Conf., Seattle, 1628–1632, 1995.

  13. I. Karatzas and S. E. Shreve, “Brownian Motion and Stochastic Calculus”, Springer, New York, 1988.

    MATH  Google Scholar 

  14. S. A. Kesller and D. A Cicci, Filtering methods for the orbit determination of a tethered satellite, The J. Astronaut. Sci., 48 (1997), 263–278.

    Google Scholar 

  15. P. E. Kloeden and E. Platen, The Numerical Solutions of Stochastic Differential Equations 23 (1991) of Applications of Mathematics, Springer, New York.

  16. P. E. Kloeden, E. Platen, and H. Schurtz, The numerical solutions of non-linear stochastic dynamical systems: a brief introduction, Int. J. Bif. Chaos, 1(1991), 277–286.

    Article  MATH  Google Scholar 

  17. C. Kurrer and K. Schulten, Effect of noise and perturbations on limit cycle systems, Physica D 50(1991), 311–320.

    Article  MATH  MathSciNet  Google Scholar 

  18. H. J. Kushner, Approximations to optimal non-linear filters, IEEE Trans. Automat. Contr. 12,(1967), 546–556.

    Article  Google Scholar 

  19. R. S. Liptser and A. N. Shiryayev, “Statistics of Random Processes 1”, Springer, Berlin, 1977.

    Google Scholar 

  20. V. S. Pugachev and I. N. Sinitsyn, “Stochastic Differential Systems (Analysis and Filtering)”, John-Wiley and Sons, New York, 1987.

    MATH  Google Scholar 

  21. D. Revuz and M. Yor, “Continuous Martingales and Brownian Motion”, Springer, Berlin, 1999.

    MATH  Google Scholar 

  22. A. P. Sage and M. L. Melsa, “Estimation Theory with Applications to Communications and Control”, Mc-Graw Hill, New York, 1971.

    MATH  Google Scholar 

  23. D. J. Scheeres, M. D. Guman, and B. F. Villac, Stability analysis of planetary satellite orbiters: application to the Europa orbiter. Journal of Guid., Contr., and Dyn., 24(2001), 778–787.

    Article  Google Scholar 

  24. Shambhu N. Sharma and H. Parthasarathy, Dynamics of a stochastically perturbed two-body problem. Pro. Royal. Soc. A: Mathematical, Physical and Engineering Sciences, London 463(2007), 979–1003.

    Article  MATH  MathSciNet  Google Scholar 

  25. Shambhu N. Sharma and H. Parthasarathy, A two-body continuous-discrete filter, Nonlinear Dynamics,(2007),(doi: 10.1007/s11071- 007-9199-0).

  26. K. R. Schenk-Hoppé, Bifurcation scenarios of the noisy Duffing-van der Pol oscillator, Nonlinear Dynamics,11(1996), 255–274.

    Article  MathSciNet  Google Scholar 

  27. K. R. Schenk-Hoppé, Stochastic Hopf bifurcation: an example, Int. J. of Non-linear Mech. 31(1996), 685–692.

    Article  MATH  Google Scholar 

  28. G.Y. Terdik, Stationary solutions for bilinear systems with constant coefficients. In Seminar on Stochastic Processes (E. Cinlar, K. L. Chug and R. K. Getoor, eds.), Birkhhauser, Boston, 196–206, 1989.

    Google Scholar 

  29. N. G. van Kampen, “Stochastic Problems in Physics and Chemistry”, North Holland, Amsterdam, 1981.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shambhu N. Sharma.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sharma, S.N. A Kolmogorov-Fokker-Planck approach for a stochastic Duffing-van der Pol system. Differ Equ Dyn Syst 16, 351–377 (2008). https://doi.org/10.1007/s12591-008-0019-x

Download citation

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12591-008-0019-x

MSC

Keywords

Navigation