Abstract
A stochastic differential equation model is considered for nonlinear oscillators under excitations of combined Gaussian and Poisson white noise. Since the solutions of stochastic differential equations can be interpreted in terms of several types of stochastic integrals, it is sometimes confusing about which integral is actually appropriate. In order for the energy conservation law to hold under combined Gaussian and Poisson white noise excitations, an appropriate stochastic integral is introduced in this paper. This stochastic integral reduces to the Di Paola–Falsone integral when the multiplicative noise intensity is infinitely differentiable with respect to the state. The stochastic integral introduced in this paper is applicable in more general situations. Numerical examples are presented to illustrate the theoretical conclusion.
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Abbreviations
- t :
-
Time
- m :
-
Mass
- k :
-
Stiffness coefficient
- x(t):
-
Displacement depending on time
- \(\dot{x}(t)\) :
-
First derivative of x(t) with respect to time t
- \(\overset{..}{ x}(t)\) :
-
Second derivative of x(t) with respect to time t
- \(g(x(t), \dot{x}(t)\) :
-
A function of x(t) and \(\dot{x}(t)\) which appears as a generalized force term
- \(f(x(t), \dot{x}(t))\) :
-
A function of x(t) and \(\dot{x}(t)\) which appears as the coefficient of noise term
- B(t):
-
Brownian motion
- \(\dot{B}(t)\) :
-
Gaussian white noise defined as the generalized time derivative of the Brownian motion B(t)
- C(t):
-
Compound Poisson process
- \(\dot{C}(t)\) :
-
Compound Poisson white noise defined as the generalized time derivative of the compound Poisson process C(t)
- N(t):
-
Poisson process
- \(\tilde{\lambda }\) :
-
Intensity parameter of the Poisson process N(t)
- b :
-
A constant representing the weight of B(t) in L(t)
- c :
-
A constant representing the weight of C(t) in L(t)
- \(U(t-t_i)\) :
-
A unit step function at time \(t_i\)
- \(\delta (t-t_i)\) :
-
Delta function at time \(t_i\)
- \(R_i\) :
-
Random variable representing the ith impulse in C(t)
- L(t):
-
A stochastic process defined as \( bB(t)+cC(t)\)
- \(\dot{L}(t)\) :
-
Combined Gaussian and Poisson white noise defined as the generalized time derivative of the stochastic process L(t)
- \(\mathbf {y} (t)\) :
-
State variable defined as \(\begin{pmatrix}x(t)\\ \dot{x}(t) \end{pmatrix}\)
- A :
-
A matrix defined as \(\begin{pmatrix} 0 &{}\quad 1\\ -\dfrac{k}{m} &{}\quad 0\end{pmatrix}\)
- \(\omega \) :
-
A variable defined as \(\sqrt{\dfrac{k}{m}}\)
- \(\star \) :
-
Ito calculus
- \(\circ \) :
-
Stratonovich calculus
- \(C(s-)\) :
-
The left limit of C(s) at s
- \(\Delta C(s)\) :
-
The jump size of C(s) at s, defined as \(C(s) - C(s-)\)
- \(\underline{\dot{x}} (t_i, r)\) :
-
The value of \(\dot{x}(s)\) at \(s=t_i\) as C(s) jumped from \(C(t_i-)\) to r
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Appendix: Proof of the energy–work law (33) from the solution (32)
Appendix: Proof of the energy–work law (33) from the solution (32)
For convenience, we introduce the following notations: \(g(s)=\dfrac{1}{m} g(x(s),\dot{x}(s))\), \(f(s)=\dfrac{1}{m} f(x(s), \dot{x}(s))\), and \(f_{\dot{x}} (s)=\dfrac{1}{m} f_{\dot{x}(s)}(x(s), \dot{x}(s))\). In this Appendix, all stochastic integrals with respect to Brownian motion are in the sense of Ito (we have dropped \(\star \) notation). Then the energy–work law (33) is equivalent to
Next, we show the solution given in (32) satisfies the energy–work law (48).
Denote the right- and left-hand sides of (48) by RHS and LHS, respectively. Substitute (32) into the left-hand side of (48), we get
Substituting (32) into the right-hand side of (48), we get
To prove LHS in (49) is equal to RHS in (50), we claim the following facts
and
One can easily see that (54) and (55) are true by using the trignometric identities
and
In the following, we give the proofs for (51) and (52). The proof of (53) is similar to those for (51) and (52) and is not given here.
To prove (51) is true, we rewrite the right-hand side of (51) as double integrals
Similarly, to prove (52), we rewrite the right-hand side (52) as
The integral domain for the right-hand side of (56) is a square given by \(A=\{(s,p) \big | s\in [0,t], p\in [0, t]\}\). Decompose the square into three parts:
and
then the right-hand side of (56) becomes
Note that
and
It follows from (58) and (59) that (52) is true.
Adding Eqs. (51) to (55), we get LHS \(=\) RHS, and hence, (33) is proved.
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Sun, X., Duan, J. & Li, X. Stochastic modeling of nonlinear oscillators under combined Gaussian and Poisson white noise: a viewpoint based on the energy conservation law. Nonlinear Dyn 84, 1311–1325 (2016). https://doi.org/10.1007/s11071-015-2570-7
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DOI: https://doi.org/10.1007/s11071-015-2570-7