Melnikov distance

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In mathematics, the Melnikov method is a tool to identify the existence of chaos in a class of dynamical systems under periodic perturbation.

Introduction

The Melnikov method is used in many cases to predict the occurrence of chaotic orbits in non-autonomous smooth nonlinear systems under periodic perturbation. According to the method, it is possible to construct a function called the "Melnikov function" which can be used to predict either regular or chaotic behavior of a dynamical system. Thus, the Melnikov function will be used to determine a measure of distance between stable and unstable manifolds in the Poincaré map. Moreover, when this measure is equal to zero, by the method, those manifolds crossed each other transversally and from that crossing the system will become chaotic.

This method appeared in 1890 by H. Poincaré [1] and by V. Melnikov in 1963[2] and could be called the "Poincaré-Melnikov Method". Moreover, it was described by several textbooks as Guckenheimer & Holmes,[3]Kuznetsov,[4] S. Wiggins,[5] Awrejcewicz & Holicke[6] and others. There are many applications for Melnikov distance as it can be used to predict chaotic vibrations.[7] In this method, critical amplitude is found by setting the distance between homoclinic orbits and stable manifolds equal to zero. Just like in Guckenheimer & Holmes where they were the first who based on the KAM theorem, determined a set of parameters of relatively weak perturbed Hamiltonian systems of two-degrees-of-freedom, at which homoclinic bifurcation occurred.

The Melnikov distance

Consider the following class of systems given by

Figure 1: Phase space representing the assumptions A1and A2with respect to the system (1).

x˙=Hy(x,y)+ϵg1(x,y,t,ϵ)y˙=Hx(x,y)+ϵg2(x,y,t,ϵ),(1)or in vector formq˙=JDH(q)+ϵg(q,t,ϵ)  (2)

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Figure 2: Homoclinic manifolds Ws(γ(t))and Wu(γ(t))indicated by Γγ.The lines on Γγrepresent a typical trajectory of the system 4.

where q=(x,y), DH=(Hx,Hy), g=(g1,g2) and


J=(0110).

Assume that system (1) is smooth on the region of interest, ϵ is a small perturbation parameter and g is a periodic vector function in twith the period T=2πω.

If ϵ=0, then there is an unperturbed system

q˙=JDH(q).  (3)

From this system (3), looking at the phase space in Figure 1, consider the following assumptions

  • A1 - The system has a hyperbolic fixed point p0, connected to itself by a homoclinic orbit  q0(t)=(x0(t),y0(t));
  • A2 - The system is filled inside Γp0by a continuous family  of periodic orbits qα(t) of period Tαwith α(1,0), where Γp0={q2|q=q0(t),t}=Ws(p0)Wu(p0){p0}.

To obtain the Melnikov function, some tricks have to be used, for example, to get rid of the time dependence and to gain geometrical advantages new coordinate has to be used ϕ that is cyclic type given by ϕ=ωt+ϕ0.Then, the system (1) could be rewritten in vector form as follows

Figure 3: Normal vector πpto Γγ.

q˙=JDH(q)+ϵg(q,ϕ,ϵ)ϕ˙=ω.  (4)

Hence, looking at Figure 2, the three-dimensional phase space 2×𝕊1,where q2and ϕ𝕊1has the hyperbolic fixed point p0of the unperturbed system becoming a periodic orbit γ(t)=(p0,ϕ(t)). The two-dimensional stable and unstable manifolds of γ(t)by Ws(γ(t))and Wu(γ(t)) are denoted, respectively. By the assumption A1, Ws(γ(t)) and Wu(γ(t))coincide along a two-dimensional homoclinic manifold. This is denoted by Γγ={(q,ϕ)2×𝕊1|q=q0(t0),t0;ϕ=ϕ0(0,2π]},where t0 is the time of flight from a point q0(t0)to the point q0(0)on the homoclinic connection.

In the Figure 3, for any point p(q0(t0),ϕ0), a vector is constructed πp, normal to the Γγas follows πp(DH(q0(t0),0).Thus varying t0and ϕ0serve to move πpto every point on Γγ.

Splitting of stable and unstable manifolds

If ϵ0 is sufficiently small, which is the system (2), then γ(t) becomes γϵ(t), Γγ becomes Γγϵ, and the stable and unstable manifolds become different from each other. Furthermore, for this sufficiently small ϵin a neighborhood 𝒩(ϵ0), the periodic orbit γ(t)of the unperturbed vector field (3) persists as a periodic orbit, γϵ(t)=γ(t)+𝒪(ϵ). Moreover, Wlocs(γϵ(t)) and Wlocu(γϵ(t)) are Cr ϵ-close to Wlocs(γ(t)) and Wlocu(γ(t)) respectively.

Figure 4: Splitting of the manifolds giving Ws(γϵ(t))and Wu(γϵ(t))as projections in Σϕ0.

Consider the following cross-section of the phase space Σϕ0={(q,ϕ)2|ϕ=ϕ0}, then (q(t),ϕ(t)) and (qϵ(t),ϕ(t)) are the trajectories of the

unperturbed and perturbed vector fields, respectively. The projections of these trajectories onto Σϕ0are given by (q(t),ϕ0(t)) and (qϵ(t),ϕ0(t)). Looking at the Figure 4, splitting of Ws(γϵ(t)) and Wu(γϵ(t)), is defined hence, consider the points that intersect πp transversely as pϵs and pϵu, respectively. Therefore, it is natural to define the distance between Ws(γϵ(t)) and Wu(γϵ(t)) at the point p, denoted by d(p,ϵ)|pϵspϵu|and it can be rewritten as d(p,ϵ)=(pϵspϵu)(DH(q0(t0),0)(DH(q0(t0),0). Since pϵsand pϵulie on πp,pϵs=(qϵs,ϕ0)and pϵu=(qϵu,ϕ0), and then d(p,ϵ) can be rewritten by

Figure 5: Geometrical representation with respect to the crossing of the manifolds to the normal vector πp.

d(t0,ϕ0,ϵ)=DH(q0(t0))(qϵuqϵs)(DH(q0(t0)).  (5)

The manifolds Ws(γϵ(t)) and Wu(γϵ(t)) may intersect πp in more than one point as shown in Figure 5. For it to be possible, after every intersection, for ϵ sufficiently small, the trajectory must pass through 𝒩(ϵ0) again.

Deduction of the Melnikov function

Expanding in Taylor series the eq. (5) about ϵ=0, gives us d(t0,ϕ0,ϵ)=d(t0,ϕ0,0)+ϵdϵ(t0,ϕ0,0)+𝒪(ϵ2), where d(t0,ϕ0,0)=0 and dϵ(t0,ϕ0,0)=DH(q0(t0))(qϵuϵ|ϵ=0qϵsϵ|ϵ=0)(DH(q0(t0)).

When d(t0,ϕ0,ϵ)=0, then the Melnikov function is defined to be

M(t0,ϕ0)DH(q0(t0))(qϵuϵ|ϵ=0qϵsϵ|ϵ=0),  (6)

since DH(q0(t0))=(Hx(q0(t0)),Hy(q0(t0)))is not zero on q0(t0), considering t0finite and M(t0,ϕ0)=0dϵ(t0,ϕ0)=0.

Using eq. (6) it will require knowing the solution to the perturbed problem. To avoid this, Melnikov defined a time dependent Melnikov function

M(t;t0,ϕ0)DH(q0(tt0))(qϵu(t)ϵ|ϵ=0qϵs(t)ϵ|ϵ=0)  (7)

Where qϵu(t) and qϵs(t) are the trajectories starting at qϵu and qϵs respectively. Taking the time-derivative of this function allows for some simplifications. The time-derivative of one of the terms in eq. (7) isddt(DH(q0(tt0))qϵu,s(t)ϵ|ϵ=0)=(D2H(q0(tt0)q0˙(tt0)))qϵu,s(t)ϵ|ϵ=0+DH(q0(tt0))ddtqϵu,s(t)ϵ|ϵ=0.  (8) From the equation of motion, q˙ϵu,s(t)=JDH(qϵu,s(t))+ϵg(qϵu,s(t),t,ϵ), then ddtqϵu,sϵ|ϵ=0=JD2H(q0(tt0))qϵu,sϵ|ϵ=0+g(q0(tt0),t,0)  (9)Plugging equations (2) and (9) back into (8) gives llddt(DH(q0(tt0))qϵu,sϵ|ϵ=0)=D2H(q0(tt0))JDH(q0(tt0)qϵu,s(t)ϵ|ϵ=0+ DH(q0(tt0))JD2H(q0(tt0))qϵu,s(t)ϵ|ϵ=0+ DH(q0(tt0))g(q0(tt0),ϕ(t),0)  (10) The first two terms on the right hand side can be verified to cancel by explicitly evaluating the matrix multiplications and dot products. g(q,t,ϵ) has been reparameterized to g(q,ϕ,ϵ).

Integrating the remaining term, the expression for the original terms does not depend on the solution of the perturbed problem.

DH(q0(τt0))qϵu(τ)ϵ|ϵ=0=τDH(q0(tt0))g(q0(tt0),ωt+ϕ0,0)dtDH(q0(τt0))qϵs(τ)ϵ|ϵ=0=τDH(q0(tt0))g(q0(tt0),ωt+ϕ0,0)dt  (11)

The lower integration bound has been chosen to be the time where qϵu,s(t)=γ(t), so that qϵu,s(t)ϵ=0 and therefore the boundary terms are zero.

Combining these terms and setting τ=0, the final form for the Melnikov distance is obtained by

M(t0,ϕ0)=+DH(q0(t))g(q0(t),ωt+ωt0+ϕ0,0)dt.  (12)

Then, using this equation, the following theorem

Theorem 1: Suppose there is a point (t0,ϕ0)=(t0¯,ϕ0¯)such that

  • i) M(t0¯,ϕ0¯)=0 and
  • ii) Mt0|(t0¯,ϕ0¯)0.

Then, for ϵ sufficiently small, Ws(γϵ(t)) and Wu(γϵ(t)) intersect transversely at (q0(t0)+𝒪(ϵ),ϕ0). Moreover, if M(t0,ϕ0)0 for all (t0,ϕ0)1×𝕊1, then Ws(γϵ(t))Wu(γϵ(t))=.

Simple zeros of the Melnikov function imply chaos

From theorem 1 when there is a simple zero of the Melnikov function implies in transversal intersections of the stable Ws(γϵ(t))and Wu(γϵ(t)) manifolds that results in a homoclinic tangle. Such tangle is a very complicated structure with the stable and unstable manifolds intersecting an infinite number of times.

Consider a small element of phase volume, departing from the neighborhood of a point near the transversal intersection, along the unstable manifold of a fixed point. Clearly, when this volume element approaches the hyperbolic fixed point it will be distorted considerably, due to the repetitive infinite intersections and stretching (and folding) associated with the relevant invariant sets. Therefore, it is reasonably expect that the volume element will undergo an infinite sequence of stretch and fold transformations as the horseshoe map. Then, this intuitive expectation is rigorously confirmed by a theorem stated as follows

Theorem 2: Suppose that a diffeomorphism P:MM, where M is an n-dimensional manifold, has a hyperbolic fixed point x¯ with a stable Ws(x¯) and Wu(x¯) unstable manifold that intersect transversely at some point x0x¯, Ws(x¯)Wu(x¯),where dimWs+dimWu=n. Then, M contains a hyperbolic set Λ, invariant under P, on which P is topologically conjugate to a shift on finitely many symbols.

Thus, according to the theorem 2, it implies that the dynamics with a transverse homoclinic point is topologically similar to the horseshoe map and it has the property of sensitivity to initial conditions and hence when the Melnikov distance (10) has a simple zero, it implies that the system is chaotic.

References

  1. Poincaré, Henri (1890). "Sur le problème des trois corps et les équations de la dynamique". Acta Mathematica 13: 1–270. http://sciences.amisbnf.org/fr/livre/sur-le-probleme-des-trois-corps-et-les-equations-de-la-dynamique. 
  2. Melnikov, V. K. (1963). "On the stability of a center for time-periodic perturbations". Tr. Mosk. Mat. Obs. 12: 3–52. 
  3. Guckenheimer, John; Holmes, Philip (1983). Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. Springer Science & Business Media. ISBN 978-1-4612-1140-2. 
  4. Aleksandrovich), Kuznet︠s︡ov, I︠U︡. A. (I︠U︡riĭ (2004). Elements of Applied Bifurcation Theory (Third ed.). New York, NY: Springer New York. ISBN 9781475739787. OCLC 851800234. 
  5. Stephen, Wiggins (2003). Introduction to applied nonlinear dynamical systems and chaos (Second ed.). New York: Springer. ISBN 978-0387217499. OCLC 55854817. 
  6. Awrejcewicz, Jan; Holicke, Mariusz M (September 2007). Smooth and Nonsmooth High Dimensional Chaos and the Melnikov-Type Methods. World Scientific Series on Nonlinear Science Series A. WORLD SCIENTIFIC. doi:10.1142/6542. ISBN 9789812709097. Bibcode2007snhd.book.....A. 
  7. Alemansour, Hamed; Miandoab, Ehsan Maani; Pishkenari, Hossein Nejat (2017-03-01). "Effect of size on the chaotic behavior of nano resonators". Communications in Nonlinear Science and Numerical Simulation 44: 495–505. doi:10.1016/j.cnsns.2016.09.010. ISSN 1007-5704. Bibcode2017CNSNS..44..495A.