Skip to main content
Log in

Stochastic stability of quasi-partially integrable and non-resonant Hamiltonian systems under parametric excitations of combined Gaussian and Poisson white noises

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

The asymptotic Lyapunov stability with probability one of multi-degree-of freedom quasi-partially integrable and non-resonant Hamiltonian systems subject to parametric excitations of combined Gaussian and Poisson white noises is studied. First, the averaged stochastic differential equations for quasi partially integrable and non-resonant Hamiltonian systems subject to parametric excitations of combined Gaussian and Poisson white noises are derived by means of the stochastic averaging method and the stochastic jump-diffusion chain rule. Then, the expression of the largest Lyapunov exponent of the averaged system is obtained by using a procedure similar to that due to Khasminskii and the properties of stochastic integro-differential equations. Finally, the stochastic stability of the original quasi-partially integrable and non-resonant Hamiltonian systems is determined approximately by using the largest Lyapunov exponent. An example is worked out in detail to illustrate the application of the proposed method. The good agreement between the analytical results and those from digital simulation show that the proposed method is effective.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ling, Q., Jin, X.L., Li, H.F., Huang, Z.L.: Lyapunov function construction for nonlinear stochastic dynamical systems. Nonlinear Dyn. 72(4), 853–864 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  2. Huang, Z.L., Jin, X.L., Zhu, W.Q.: Lyapunov functions for quasi-Hamiltonian systems. Probab. Eng. Mech. 24(3), 374–381 (2009)

    Article  Google Scholar 

  3. Grigoriu, M.: Lyapunov exponents for nonlinear systems with Poisson white noise. Phys. Lett. A 217(4), 258–262 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  4. Zhu, W.Q.: Lyapunov exponents and stochastic stability of quasi-non-integrable Hamiltonian systems. Int. J. Non-Linear Mech. 21, 569–579 (2004)

    Article  Google Scholar 

  5. Zhu, W.Q., Huang, Z.L., Suzuki, Y.: Stochastic averaging and Lyapunov exponent of quasi partially integrable Hamiltonian systems. Int. J. Non-Linear Mech. 37, 419–437 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  6. Zhu, W.Q., Huang, Z.L.: Lyapunov exponents and stochastic stability of quasi-integrable-Hamiltonian systems. J. Appl. Mech. 66, 211–217 (1992)

    Article  MathSciNet  Google Scholar 

  7. Khasminskii, R.Z.: Sufficient and necessary conditions of almost sure asymptotic stability of a linear stochastic system. Theory Probab. Appl. 12, 144–147 (1967)

    Article  Google Scholar 

  8. Kozin, F., Zhang, Z.Y.: On almost sure sample stability of nonlinear differential equations. Probab. Eng. Mech. 6(2), 92–95 (1991)

    Article  Google Scholar 

  9. Roberts, J.B., Spanos, P.D.: Stochastic averaging: an approximate method of solving random vibration problems. Int. J. Non-Linear Mech. 21, 111–134 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  10. Zhu, W.Q.: Stochastic averaging method in random vibration. J. Appl. Mech. Rev. 41(5), 189–199 (1988)

    Article  Google Scholar 

  11. Zhu, W.Q.: Recent developments and applications of stochastic averaging method in random vibration. Appl. Mech. Rev. 49(10), s72–s80 (1996)

    Article  Google Scholar 

  12. Ariaratnam, S.T., Tam, D.S.F.: Lyapunov exponents and stochastic stability of coupled linear system. Probab. Eng. Mech. 6, 151–156 (1991)

    Article  Google Scholar 

  13. Ariaratnam, S.T., Xie, W.C.: Lyapunov exponents and stochastic stability of coupled linear system under real noise excitation. J. Appl. Mech. 59, 664–673 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  14. Xu, Y., Wang, X.Y., Zhang, H.Q., Xu, W.: Stochastic stability for nonlinear systems deriven by L\(\acute{\text{ e }}\)vy noise. Nonlinear Dyn. 68(1–2), 7–15 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  15. Wojtkiwicz, S.F., Johnson, E.A., Bergman, L.A., Grigoriu, M., Spencer, B.F.: Response of stochastic dynamics systems deriven by additive Gaussian and Poisson white noise: solution of a forward generalized Kolmogorov equation by a spectral finite difference method. Comput. Methods Appl. Mech. Eng. 168, 73–89 (1999)

    Article  Google Scholar 

  16. Zhu, H.T., Er, G.K., Iu, V.P., Kou, K.P.: Probabilistic solution of nonlinear oscillators excited by combined Gaussian and Poisson white noises. J. Sound Vib. 330, 2900–2909 (2011)

    Article  Google Scholar 

  17. Jia, W.T., Zhu, W.Q., Xu, Y.: Stochastic averaging of quasi-non-integrable Hamiltonian systems under combined Gaussian and Poisson white noise excitations. Int. J. Non-Linear Mech. 51, 45–53 (2013)

  18. Jia, W.T., Zhu, W.Q., Xu, Y., Liu, W.Y.: Stochastic averaging of quasi-integrable and resonant Hamiltonian systems under combined Gaussian and Poisson white noise excitations. J. Appl. Mech. 81(4), 014009 (2014)

    Google Scholar 

  19. Jia, W.T., Zhu, W.Q.: Stochastic averaging of quasi-partially integrable Hamiltonian systems under combined Gaussian and Poisson white noise excitations. J. Phys. A 398, 125–144 (2014)

    MathSciNet  Google Scholar 

  20. Jia, W.T., Zhu, W.Q.: Stochastic averaging of quasi-integrable and non-resonant Hamiltonian systems under combined Gaussian and Poisson white noise excitations. Nonlinear Dyn. 76(2), 1271–1289 (2014). doi:10.1007/s11071-013-1209-9

    Article  MathSciNet  Google Scholar 

  21. Liu, W.Y., Zhu, W.Q., Xu, W.: Stochastic stability of quasi non-integrable Hamiltonian systems under parametric excitations of Gaussian and Poisson white noises. Probab. Eng. Mech. 32, 39–47 (2013)

    Article  Google Scholar 

  22. Liu, W.Y., Zhu, W.Q., Jia, W.T.: Stochastic stability of quasi-integrable and non-resonant Hamiltonian systems under parametric excitations of combined Gaussian and Poisson white noises. Int. J. Non-Linear Mech. 58, 191–198 (2014)

    Article  Google Scholar 

  23. Di Paola, M., Falsone, G.: \(It\hat{o}\) and Stratonovich integrals for delta-correlated processes. Probab. Eng. Mech. 8(3), 197–208 (1993)

    Article  Google Scholar 

  24. Lin, Y.K., Cai, G.Q.: Probabilistic Structural Dynamics: Advanced Theory and Applications. McGraw-Hill, New York (1993)

    Google Scholar 

  25. Di Paola, M., Vasta, M.: Stochastic integro-differential and differential equations of non-linear systems excited by parametric Poisson pulses. Int. J. Non-Linear Mech. 32(5), 855–862 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  26. Hanson, F.B.: Applied Stochastic Processes and Control for Jump-Diffusions: Modeling, Analysis, ZND Computation. SIAM, Philadelphia (2007)

    Book  Google Scholar 

  27. Di Paola, M., Falsone, G.: Stochastic dynamics of nonlinear systems driven by non-normal delta-correlated process. J. Appl. Mech. 60, 141–148 (1993)

  28. Khasminskii, R.Z.: On the averaging principle for stochastic \(It\hat{o}\) equation. Kibernetika 4, 260–279 (1968)

    Google Scholar 

  29. Zhu, W.Q., Yang, Y.Q.: Stochastic averaging of quasi-non-integrable-Hamiltonian systems. J. Appl. Mech. 64, 157–164 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  30. Zeng, Y., Zhu, W.Q.: Stochastic averaging of quasi-nonintegrable-Hamiltonian systems under Poisson white noise excitation. J. Appl. Mech. 78(2), 021002 (2011)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The work reported in this paper is supported by the National Natural Science Foundation of China under Grant Nos. 10932009, 11072212, 11272279, and the Basic Research Fund of Northwestern Polytechnic University under Grant No. JC201242.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Weiqiu Zhu.

Appendices

Appendix 1

The SDEs for system (33)are:

$$\begin{aligned}&{\text {d}}{Q_1} = \frac{{\partial H}}{{\partial {P_1}}}{\text {d}}t ,\nonumber \\&{\text {d}}{P_1} = \Big ( - \frac{{\partial H}}{{\partial {Q_1}}} - {\beta _{11}}{P_1} - {\beta _{12}}{P_2} - {\beta _{13}}{P_3} \Big ){\text {d}}t \nonumber \\&\quad + {f_{11}}{P_1}{\text {d}}{B_1}(t) + {f_{12}}{P_2}{\text {d}}{B_2}(t) + {f_{13}}{P_3}{\text {d}}{B_3}(t) \nonumber \\&\quad + {g_{11}}{P_1}{\text {d}}{C_1}(t) + {g_{12}}{P_2}{\text {d}}{C_2}(t)\nonumber \\&\quad + {g_{13}}{P_3}{\text {d}}{C_3}(t) + \frac{1}{2}g_{11}^2{P_1}{[{\text {d}}{C_1}(t)]^2} \nonumber \\&\quad + \frac{1}{2}{g_{12}}{g_{22}}{P_2}{[{\text {d}}{C_2}(t)]^2} + \frac{1}{2}{g_{13}}{g_{33}}{P_3}{[{\text {d}}{C_3}(t)]^2} \nonumber \\&\quad +\ \frac{1}{6}g_{11}^3{P_1}{[{\text {d}}{C_1}(t)]^3} + \frac{1}{6}{g_{12}}g_{22}^2{P_2}{[{\text {d}}{C_2}(t)]^3} \nonumber \\&\quad + \frac{1}{6}{g_{13}}g_{33}^2{P_3}{[{\text {d}}{C_3}(t)]^3} + \frac{1}{{24}}g_{11}^4{P_1}{[{\text {d}}{C_1}(t)]^4} \nonumber \\&\quad + \frac{1}{{24}}{g_{12}}g_{22}^3{P_2}{[{\text {d}}{C_2}(t)]^4} \!+\! \frac{1}{{24}}{g_{13}}g_{33}^3{P_3}{[{\text {d}}{C_3}(t)]^4} \!+\! \cdots ,\nonumber \\&{\text {d}}{Q_2} = \frac{{\partial H}}{{\partial {P_2}}}{\text {d}}t, \nonumber \\&{\text {d}}{P_2} =\Big ( - \frac{{\partial H}}{{\partial {Q_2}}} - {\beta _{21}}{P_1} - {\beta _{22}}{P_2} - {\beta _{23}}{P_3}\Big ){\text {d}}t \nonumber \\&\quad + {f_{21}}{P_1}{\text {d}}{B_1}(t) + {f_{22}}{P_2}{\text {d}}{B_2}(t) + {f_{23}}{P_3}{\text {d}}{B_3}(t) \nonumber \\&\quad + {g_{21}}{P_1}{\text {d}}{C_1}(t) + {g_{22}}{P_2}{\text {d}}{C_2}(t) + {g_{23}}{P_3}{\text {d}}{C_3}(t)\nonumber \\&\quad + \frac{1}{2}{g_{21}}{g_{11}}{P_1}{[{\text {d}}{C_1}(t)]^2}+ \frac{1}{2}g_{22}^2{P_2}{[{\text {d}}{C_2}(t)]^2}\nonumber \\&\quad + \frac{1}{2}{g_{23}}{g_{33}}{P_3}{[{\text {d}}{C_3}(t)]^2} \nonumber \\&\quad + \frac{1}{6}{g_{21}}g_{11}^2{P_1}{[{\text {d}}{C_1}(t)]^3} + \frac{1}{6}g_{22}^3{P_2}{[{\text {d}}{C_2}(t)]^3} \nonumber \\&\quad + \frac{1}{6}{g_{23}}g_{33}^2{P_3}{[{\text {d}}{C_3}(t)]^3} + \frac{1}{{24}}{g_{21}}g_{11}^3{P_1}{[{\text {d}}{C_1}(t)]^4} \nonumber \\&\quad + \frac{1}{{24}}g_{22}^4{P_2}{[{\text {d}}{C_2}(t)]^4} + \frac{1}{{24}}{g_{23}}g_{33}^3{P_3}{[{\text {d}}{C_3}(t)]^4} + \cdots , \nonumber \\&{\text {d}}{Q_3} = \frac{{\partial H}}{{\partial {P_3}}}{\text {d}}t,\nonumber \\&{\text {d}}{P_3} = \Big ( - \frac{{\partial H}}{{\partial {Q_3}}} - {\beta _{31}}{P_1} - {\beta _{32}}{P_2} - {\beta _{33}}{P_3}\Big ){\text {d}}t \nonumber \\&\quad + {f_{31}}{P_1}{\text {d}}{B_1}(t) + {f_{32}}{P_2}{\text {d}}{B_2}(t) + {f_{33}}{P_3}{\text {d}}{B_3}(t) \nonumber \\&\quad + {g_{31}}{P_1}{\text {d}}{C_1}(t) + {g_{32}}{P_2}{\text {d}}{C_2}(t) \nonumber \\&\quad + {g_{33}}{P_3}{\text {d}}{C_3}(t) + \frac{1}{2}{g_{31}}{g_{11}}{P_1}{[{\text {d}}{C_1}(t)]^2} \nonumber \\&\quad + \frac{1}{2}{g_{32}}{g_{22}}{P_2}{[{\text {d}}{C_2}(t)]^2} + \frac{1}{2}g_{33}^2{P_3}{[{\text {d}}{C_3}(t)]^2} \nonumber \\&\quad + \frac{1}{6}{g_{31}}g_{11}^2{P_1}{[{\text {d}}{C_1}(t)]^3} + \frac{1}{6}{g_{32}}g_{22}^2{P_2}{[{\text {d}}{C_2}(t)]^3} \nonumber \\&\quad + \frac{1}{6}g_{33}^3{P_3}{[{\text {d}}{C_3}(t)]^3} + \frac{1}{{24}}{g_{31}}g_{11}^3{P_1}{[{\text {d}}{C_1}(t)]^4} \nonumber \\&\quad + \frac{1}{{24}}{g_{32}}g_{22}^3{P_2}{[{\text {d}}{C_2}(t)]^4} \!+\! \frac{1}{{24}}g_{33}^4{P_3}{[{\text {d}}{C_3}(t)]^4} \!+\! \cdots . \end{aligned}$$
(48)

Appendix 2

The other coefficients of Eqs. (36) and (37) are:

$$\begin{aligned}&{a_{11}} = f_{11}^2{D_1} - {\beta _{11}} + g_{11}^2{\lambda _1}E[Y_1^2] + \frac{1}{3}g_{11}^4{\lambda _1}E[Y_1^4], \nonumber \\&{a_{12}} = [f_{12}^2{D_2} + f_{13}^2{D_3} + \frac{1}{2}g_{12}^2{\lambda _2}E[Y_2^2] \nonumber \\&\quad + \frac{1}{2}g_{13}^2{\lambda _3}E[Y_3^2] \nonumber \\&\quad + \frac{7}{{24}}(g_{12}^2g_{22}^2{\lambda _2}E[Y_2^4] + g_{13}^2g_{33}^2{\lambda _3}E[Y_3^4])]\frac{\gamma }{{1 + \gamma }}, \nonumber \\&{a_{21}} = (f_{21}^2 + f_{31}^2){D_1} + \frac{1}{2}(g_{21}^2 + g_{31}^2){\lambda _1}E[Y_1^2]\nonumber \\&\quad + \frac{7}{{24}}(g_{21}^2g_{11}^2 + g_{31}^2g_{11}^2){\lambda _1}E[Y_1^4] , \nonumber \\&{a_{22}} = [(f_{22}^2 + f_{32}^2){D_2} + (f_{23}^2 + f_{33}^2){D_3} - {\beta _{22}} - {\beta _{33}} \nonumber \\&\quad + \left( g_{22}^2 + \frac{1}{2}g_{32}^2\right) {\lambda _2}E[Y_2^2] + \left( g_{33}^2 + \frac{1}{2}g_{23}^2\right) {\lambda _3}E[Y_3^2] \nonumber \\&\quad +\left( \frac{1}{3}g_{22}^4 + \frac{1}{{12\pi }}{g_{32}}g_{23}^3 + \frac{7}{{24}}g_{22}^2g_{32}^2\right) {\lambda _2}E[Y_2^4]\nonumber \\&\quad + \left( \frac{1}{3}g_{33}^4 + \frac{1}{{12\pi }}{g_{23}}g_{33}^3 + \frac{7}{{24}}g_{23}^2g_{33}^2\right) {\lambda _3}E[Y_3^4]]\frac{\gamma }{{1 + \gamma }}, \nonumber \\&{b_{11}} = 3f_{11}^2D_1 , \nonumber \\&{b_{12}} = 2(f_{12}^2{D_2} + f_{13}^2{D_3})\frac{\gamma }{{1 + \gamma }} , \nonumber \\&{b_{21}} = 2(f_{21}^2 + f_{31}^2){D_1}\frac{\gamma }{{1 + \gamma }}, \nonumber \\&{b_{22}} = [3(f_{22}^2{D_2} + f_{33}^2{D_3}) + f_{32}^2{D_2} + f_{23}^2{D_3}]\nonumber \\&\qquad \quad \times \frac{{2{\gamma ^2}}}{{(1 + \gamma )(1 + 2\gamma )}},\nonumber \\&\varepsilon {V_{111}} = {\left( \frac{{35{\lambda _1}E[Y_1^4]}}{{8{M_{14}}}}\right) ^{\frac{1}{4}}}{g_{11}}{H_1} \nonumber \\&\varepsilon {V_{121}} = - {\left( \frac{{{M_{13}}}}{{{M_{23}}}}\right) ^{\frac{1}{3}}}\varepsilon {V_{111}} \nonumber \\&{\varepsilon ^2}{V_{122}} = \frac{{5g_{11}^4{\lambda _1}E[Y_1^4]}}{{2{M_{23}}{{(\varepsilon {V_{121}})}^2}}}H_1^3 \nonumber \\&\varepsilon {V_{131}} = \Big \{ \frac{1}{{{M_{32}}}}\Big [\frac{3}{2}g_{11}^2{\lambda _1}E[Y_1^2]H_1^2 - {(\varepsilon {V_{111}})^2}{M_{12}} \nonumber \\&\qquad \qquad - {(\varepsilon {V_{121}})^2}{M_{22}}\Big ]\Big \} ^{\frac{1}{2}} \nonumber \\&{\varepsilon ^2}{V_{132}} = - \frac{{(\varepsilon {V_{121}})({\varepsilon ^2}{V_{122}}){M_{22}}}}{{(\varepsilon {V_{131}}){M_{32}}}} \nonumber \\&{\varepsilon ^3}{V_{133}} = \frac{{\frac{7}{2}g_{11}^4{\lambda _1}E[Y_1^4]H_1^2 - {{({\varepsilon ^2}{V_{122}})}^2}{M_{22}} - {{({\varepsilon ^2}{V_{132}})}^2}{M_{32}}}}{{2{M_{32}}(\varepsilon {V_{131}})}} \nonumber \\&\varepsilon {V_{211}} = \frac{{\gamma {H_2}}}{{{{\left[ {(1 + \gamma )(1 + 2\gamma )(1 + 3\gamma )(1 + 4\gamma ){M_{14}}} \right] }^{\frac{1}{4}}}}} \nonumber \\&\quad \times {\{ {(105g_{22}^4 \!+\! 9g_{32}^4){\lambda _2}E[Y_2^4] + \left( 105g_{33}^4 + 9g_{23}^4\right) {\lambda _3}E[Y_3^4]} \}^{\frac{1}{4}}}\nonumber \\&\varepsilon {V_{221}} = - {\left( \frac{{{M_{13}}}}{{{M_{23}}}}\right) ^{\frac{1}{3}}}\varepsilon {V_{211}} \nonumber \\&{\varepsilon ^2}{V_{222}} = \frac{{{\gamma ^3}H_2^3}}{{(1 + \gamma )(1 + 2\gamma )(1 + 3\gamma ){M_{23}}{{(\varepsilon {V_{221}})}^2}}} \nonumber \\&\quad \times \Big \{ [15(2g_{22}^4 + g_{22}^2g_{32}^2) + 3(g_{32}^4 + 4g_{22}^2g_{32}^2) \nonumber \\&\quad + \frac{{16}}{\pi }(2{g_{22}}g_{32}^3 + 5g_{22}^3{g_{32}} + \frac{1}{2}g_{32}^3{g_{22}})]{\lambda _2}E[Y_2^4] \nonumber \\&\quad + [15(2g_{33}^4 + g_{23}^2g_{33}^2) + 3(g_{23}^4 + 4g_{23}^2g_{33}^2) \nonumber \\&\quad + \frac{{16}}{\pi }(2{g_{33}}g_{23}^3 + 5g_{33}^3{g_{23}} + \frac{1}{2}g_{23}^3{g_{33}})]{\lambda _3}E[Y_3^4]\Big \} \nonumber \\&\varepsilon {V_{231}} =\Big \{ \frac{1}{{{M_{32}}}}[(3g_{22}^2 + g_{32}^2 + \frac{8}{\pi }{g_{22}}{g_{32}}){\lambda _2}E[Y_2^2] + (g_{23}^2 \nonumber \\& + 3g_{33}^2 + \frac{8}{\pi }{g_{23}}{g_{33}}){\lambda _3}E[Y_3^2]]\frac{{{\gamma ^2}}}{{(1 + \gamma )(1 + 2\gamma )}}H_2^2 \nonumber \\& - {(\varepsilon {V_{211}})^2}{M_{12}} - {(\varepsilon {V_{221}})^2}{M_{22}}]{\Big \} ^{\frac{1}{2}}}\nonumber \\&{\varepsilon ^2}{V_{232}} = - \frac{{(\varepsilon {V_{221}})({\varepsilon ^2}{V_{222}}){M_{22}}}}{{(\varepsilon {V_{231}}){M_{32}}}} \nonumber \\&{\varepsilon ^3}{V_{233}} = \frac{1}{{2{M_{32}}(\varepsilon {V_{231}})}}\Big \{ [(7g_{22}^4 + \frac{{79}}{{12}}g_{22}^2g_{32}^2 + \frac{3}{4}g_{32}^4 \nonumber \\&\quad + \frac{{32}}{{3\pi }}g_{22}^3{g_{32}} + \frac{6}{\pi }g_{32}^3{g_{22}}){\lambda _2}E[Y_2^4] + (7g_{33}^4 \nonumber \\&\quad + \frac{{79}}{{12}}g_{23}^2g_{33}^2 + \frac{3}{4}g_{23}^4 + \frac{{32}}{{3\pi }}g_{33}^3{g_{23}} + \frac{6}{\pi }g_{23}^3{g_{33}}) \nonumber \\&\quad \times {\lambda _3}E[Y_3^4]]\frac{{{\gamma ^2}}}{{(1 + \gamma )(1 + 2\gamma )}}H_2^2 \nonumber \\&\quad - {({\varepsilon ^2}{V_{222}})^2}{M_{22}} - {({\varepsilon ^2}{V_{232}})^2}{M_{32}}\Big \}. \end{aligned}$$
(49)

Appendix 3

The other coefficients of Eq. (47) are:

$$\begin{aligned}&{L_{\delta 1}} = - \frac{{E[{{(\varepsilon {{V'}_{\delta 11}}(0){\alpha _\delta })}^2}]}}{2}{M_{12}}\nonumber \\&\qquad + \frac{{E[{{(\varepsilon {{V'}_{\delta 11}}(0){\alpha _\delta })}^3}]}}{3}{M_{13}} \nonumber \\&\qquad - \frac{{E[{{(\varepsilon {{V'}_{\delta 11}}(0){\alpha _\delta })}^4}]}}{4}{M_{14}} \nonumber \\&\quad = - \frac{{\int \nolimits _0^1 {{{[\varepsilon {{V'}_{\delta 11}}(0){\alpha _\delta }]}^2}p({\alpha _1}){\text {d}}{\alpha _1}} }}{2}{M_{12}} \nonumber \\&\qquad + \frac{{\int \nolimits _0^1 {{{[\varepsilon {{V'}_{\delta 11}}(0){\alpha _\delta }]}^3}p({\alpha _1}){\text {d}}\alpha _1 } }}{3}{M_{13}} \nonumber \\&\qquad - \frac{{\int \nolimits _0^1 {{{[\varepsilon {{V'}_{\delta 11}}(0){\alpha _\delta }]}^4}p({\alpha _1}){\text {d}}{\alpha _1}} }}{4}{M_{14}} \nonumber \\&{L_{\delta 2}} = - \frac{{E[{{((\varepsilon {{V'}_{\delta 21}}(0) + {\varepsilon ^2}{{V'}_{\delta 22}}(0)){\alpha _\delta })}^2}]}}{2}{M_{22}} \nonumber \\&\qquad + \frac{{E[{{((\varepsilon {{V'}_{\delta 21}}(0) + {\varepsilon ^2}{{V'}_{\delta 22}}(0)){\alpha _\delta })}^3}]}}{3}{M_{23}} \nonumber \\&\quad = - \frac{{\int \nolimits _0^1 {{{\{ [\varepsilon {{V'}_{\delta 21}}(0) + {\varepsilon ^2}{{V'}_{\delta 22}}(0)]{\alpha _\delta }\} }^2}} p({\alpha _1}){\text {d}}{\alpha _1}}}{2}{M_{22}} \nonumber \\&\qquad + \frac{{\int \nolimits _0^1 {{{\{ [\varepsilon {{V'}_{\delta 21}}(0) + {\varepsilon ^2}{{V'}_{\delta 22}}(0)]{\alpha _\delta }\} }^3}p({\alpha _1}} ){\text {d}}{\alpha _1}}}{3}{M_{23}} \nonumber \\&{L_{\delta 3}} = - \frac{{E[{{( (\varepsilon {{V'}_{\delta 31}}(0) + {\varepsilon ^2}{{V'}_{\delta 32}}(0) + {\varepsilon ^3}{{V'}_{\delta 33}}(0)){\alpha _\delta })}^2}]}}{2}{M_{32}} \nonumber \\&\quad = - \frac{{{M_{32}}}}{2} \int \limits _0^1 {{{\{ \left[ \varepsilon {{V'}_{\delta 31}}(0) + {\varepsilon ^2}{{V'}_{\delta 32}}(0) + {\varepsilon ^3}{{V'}_{\delta 33}}(0)\right] {\alpha _\delta }\} }^2}} \nonumber \\&\qquad \times p({\alpha _1} ){\text {d}}{\alpha _1}. \end{aligned}$$
(50)

Appendix 4

In Figs. 23 and 4, the simulation results for stability domain boundary presented by points are obtained as follows. First, draw the theoretical boundary in plane \((\beta _{11}, \beta _{22}+\beta _{33})\) using Eq. (47) with the given parameters. Then, for every \(\beta _{11} \), take one value of \(\beta _{22}+\beta _{33}\) near the theoretical boundary, and solve Eq. (33) with small initial system state near trivial solution using the fourth-order Runger–Kutta method. After long time, if system state approaches to trivial solution, then the point \((\beta _{11}, \beta _{22}+\beta _{33})\) is in stable domain. Otherwise, it is in unstable domain. The procedure of this digital simulation please see Appendix B of [21]. It has been described in [21] in detail.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, W., Zhu, W., Jia, W. et al. Stochastic stability of quasi-partially integrable and non-resonant Hamiltonian systems under parametric excitations of combined Gaussian and Poisson white noises. Nonlinear Dyn 77, 1721–1735 (2014). https://doi.org/10.1007/s11071-014-1413-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-014-1413-2

Keywords

Navigation