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

Background

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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

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Consider the following class of systems given by

 
Figure 1: Phase space representing the assumptions  and  with respect to the system (1).

 or in vector form 

 
Figure 2: Homoclinic manifolds  and  indicated by  The lines on  represent a typical trajectory of the system 4.

where  ,  ,   and


 

Assume that system (1) is smooth on the region of interest,   is a small perturbation parameter and   is a periodic vector function in  with the period  .

If  , then there is an unperturbed system

 

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

  • A1 - The system has a hyperbolic fixed point  , connected to itself by a homoclinic orbit   
  • A2 - The system is filled inside  by a continuous family  of periodic orbits   of period  with   where  

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   that is cyclic type given by  Then, the system (1) could be rewritten in vector form as follows

 
Figure 3: Normal vector  to  .

 

Hence, looking at Figure 2, the three-dimensional phase space  where  and  has the hyperbolic fixed point  of the unperturbed system becoming a periodic orbit   The two-dimensional stable and unstable manifolds of  by  and   are denoted, respectively. By the assumption     and  coincide along a two-dimensional homoclinic manifold. This is denoted by  where   is the time of flight from a point  to the point  on the homoclinic connection.

In the Figure 3, for any point   a vector is constructed  , normal to the  as follows  Thus varying  and  serve to move  to every point on  

Splitting of stable and unstable manifolds

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If   is sufficiently small, which is the system (2), then   becomes     becomes   and the stable and unstable manifolds become different from each other. Furthermore, for this sufficiently small  in a neighborhood   the periodic orbit  of the unperturbed vector field (3) persists as a periodic orbit,   Moreover,   and   are    -close to   and   respectively.

 
Figure 4: Splitting of the manifolds giving  and  as projections in  

Consider the following cross-section of the phase space   then   and   are the trajectories of the

unperturbed and perturbed vector fields, respectively. The projections of these trajectories onto  are given by   and   Looking at the Figure 4, splitting of   and   is defined hence, consider the points that intersect   transversely as   and  , respectively. Therefore, it is natural to define the distance between   and   at the point   denoted by  and it can be rewritten as   Since  and  lie on  and   and then   can be rewritten by

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

 

The manifolds   and   may intersect   in more than one point as shown in Figure 5. For it to be possible, after every intersection, for   sufficiently small, the trajectory must pass through   again.

Deduction of the Melnikov function

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Expanding in Taylor series the eq. (5) about   gives us   where   and  

When   then the Melnikov function is defined to be

 

since  is not zero on  , considering  finite and  

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

 

Where   and   are the trajectories starting at   and   respectively. Taking the time-derivative of this function allows for some simplifications. The time-derivative of one of the terms in eq. (7) is  From the equation of motion,   then  Plugging equations (2) and (9) back into (8) gives   The first two terms on the right hand side can be verified to cancel by explicitly evaluating the matrix multiplications and dot products.   has been reparameterized to  .

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

 

The lower integration bound has been chosen to be the time where  , so that   and therefore the boundary terms are zero.

Combining these terms and setting   the final form for the Melnikov distance is obtained by

 

Then, using this equation, the following theorem

Theorem 1: Suppose there is a point  such that

  • i)   and
  • ii)  .

Then, for   sufficiently small,   and   intersect transversely at   Moreover, if   for all  , then  

Simple zeros of the Melnikov function imply chaos

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From theorem 1 when there is a simple zero of the Melnikov function implies in transversal intersections of the stable  and   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  , where   is an n-dimensional manifold, has a hyperbolic fixed point   with a stable   and   unstable manifold that intersect transversely at some point  ,  where   Then,   contains a hyperbolic set  , invariant under  , on which   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

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  1. ^ Poincaré, Henri (1890). "Sur le problème des trois corps et les équations de la dynamique". Acta Mathematica. 13: 1–270.
  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.{{cite book}}: CS1 maint: multiple names: authors list (link)
  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. Bibcode:2007snhd.book.....A. doi:10.1142/6542. ISBN 9789812709097. {{cite book}}: |journal= ignored (help)
  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. Bibcode:2017CNSNS..44..495A. doi:10.1016/j.cnsns.2016.09.010. ISSN 1007-5704.