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Hopf bifurcation

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Complex eigenvalues of an arbitrary map (dots). In case of the Hopf bifurcation, two complex conjugate eigenvalues cross the imaginary axis.

In the mathematical theory of bifurcations, a Hopf bifurcation is a critical point where, as a parameter changes, a system's stability switches and a periodic solution arises.[1] More accurately, it is a local bifurcation in which a fixed point of a dynamical system loses stability, as a pair of complex conjugate eigenvalues—of the linearization around the fixed point—crosses the complex plane imaginary axis as a parameter crosses a threshold value. Under reasonably generic assumptions about the dynamical system, the fixed point becomes a small-amplitude limit cycle as the parameter changes.

A Hopf bifurcation is also known as a Poincaré–Andronov–Hopf bifurcation, named after Henri Poincaré, Aleksandr Andronov and Eberhard Hopf.

Overview

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Supercritical and subcritical Hopf bifurcations

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Dynamics of the Hopf bifurcation near . Possible trajectories in red, stable structures in dark blue and unstable structures in dashed light blue. Supercritical Hopf bifurcation: 1a) stable fixed point 1b) unstable fixed point, stable limit cycle 1c) phase space dynamics. Subcritical Hopf bifurcation: 2a) stable fixed point, unstable limit cycle 2b) unstable fixed point 2c) phase space dynamics. determines the angular dynamics and therefore the direction of winding for the trajectories.

The limit cycle is orbitally stable if a specific quantity called the first Lyapunov coefficient is negative, and the bifurcation is supercritical. Otherwise it is unstable and the bifurcation is subcritical.

The normal form of a Hopf bifurcation is the following time-dependent differential equation:

where zb are both complex and λ is a real parameter.

Write: The number α is called the first Lyapunov coefficient.

  • If α is negative then there is a stable limit cycle for λ > 0:
where
The bifurcation is then called supercritical.
  • If α is positive then there is an unstable limit cycle for λ < 0. The bifurcation is called subcritical.

Intuition

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Normal form of the supercritical Hopf bifurcation in Cartesian coordinates.[2]

The normal form of the supercritical Hopf bifurcation can be expressed intuitively in polar coordinates,

where is the instantaneous amplitude of the oscillation and is its instantaneous angular position.[3] The angular velocity is fixed. When , the differential equation for has an unstable fixed point at and a stable fixed point at . The system thus describes a stable circular limit cycle with radius and angular velocity . When then is the only fixed point and it is stable. In that case, the system describes a spiral that converges to the origin.

Cartesian coordinates

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The polar coordinates can be transformed into Cartesian coordinates by writing and .[3] Differentiating and with respect to time yields the differential equations,

and

Subcritical case

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The normal form of the subcritical Hopf is obtained by negating the sign of ,

which reverses the stability of the fixed points in . For the limit cycle is now unstable and the origin is stable.

Example

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The Hopf bifurcation in the Selkov system (see article). As the parameters change, a limit cycle (in blue) appears out of a stable equilibrium.

Hopf bifurcations occur in the Lotka–Volterra model of predator–prey interaction (known as paradox of enrichment), the Hodgkin–Huxley model for nerve membrane potential,[4] the Selkov model of glycolysis,[5] the Belousov–Zhabotinsky reaction, the Lorenz attractor, the Brusselator, and in classical electromagnetism.[6] Hopf bifurcations have also been shown to occur in fission waves.[7]

The Selkov model is

The figure shows a phase portrait illustrating the Hopf bifurcation in the Selkov model.[8]

In railway vehicle systems, Hopf bifurcation analysis is notably important. Conventionally a railway vehicle's stable motion at low speeds crosses over to unstable at high speeds. One aim of the nonlinear analysis of these systems is to perform an analytical investigation of bifurcation, nonlinear lateral stability and hunting behavior of rail vehicles on a tangent track, which uses the Bogoliubov method.[9]

Serial expansion method

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[10]

Consider a system defined by , where is smooth and is a parameter. After a linear transform of parameters, we can assume that as increases from below zero to above zero, the origin turns from a spiral sink to a spiral source.

Now, for , we perform a perturbative expansion using two-timing: where is "slow-time" (thus "two-timing"), and are functions of . By an argument with harmonic balance (see [10] for details), we can use . Then, plugging in to , and expanding up to the order, we would obtain three ordinary differential equations in .

The first equation would be of form , which gives the solution , where are "slowly varying terms" of . Plugging it into the second equation, we can solve for .

Then plugging into the third equation, we would have an equation of form , with the right-hand-side a sum of trigonometric terms. Of these terms, we must set the "resonance term" -- that is, -- to zero. This is the same idea as Poincaré–Lindstedt method. This then provides two ordinary differential equations for , allowing one to solve for the equilibrium value of , as well as its stability.

Example

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Consider the system defined by and . The system has an equilibrium point at origin. When increases from negative to positive, the origin turns from a stable spiral point to an unstable spiral point.

First, we eliminate from the equations:Now, perform the perturbative expansion as described above:with . Expanding up to order , we obtain:First equation has solution . Here are respectively the "slow-varying amplitude" and "slow-varying phase" of the simple oscillation.

Second equation has solution , where are also slow-varying amplitude and phase. Now, since , we can merge the two terms as some .

Thus, without loss of generality, we can assume . ThusPlug into the third equation, we obtainEliminating the resonance terms, we obtain The first equation shows that is a stable equilibrium. Thus we find that the Hopf bifurcation creates an attracting (rather than repelling) limit cycle.

Plugging in , we have . We can repick the origin of time to make . Now solve for yieldingPlugging in back to the expressions for , we havePlugging them back to yields the serial expansion of as well, up to order .

Letting for notational neatness, we have

This provides us with a parametric equation for the limit cycle. This is plotted in the illustration on the right.

Definition of a Hopf bifurcation

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The appearance or the disappearance of a periodic orbit through a local change in the stability properties of a fixed point is known as the Hopf bifurcation. The following theorem works for fixed points with one pair of conjugate nonzero purely imaginary eigenvalues. It tells the conditions under which this bifurcation phenomenon occurs.

Theorem (see section 11.2 of [11]). Let be the Jacobian of a continuous parametric dynamical system evaluated at a steady point . Suppose that all eigenvalues of have negative real part except one conjugate nonzero purely imaginary pair . A Hopf bifurcation arises when these two eigenvalues cross the imaginary axis because of a variation of the system parameters.

Routh–Hurwitz criterion

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Routh–Hurwitz criterion (section I.13 of [12]) gives necessary conditions so that a Hopf bifurcation occurs.[13]

Sturm series

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Let be Sturm series associated to a characteristic polynomial . They can be written in the form:

The coefficients for in correspond to what is called Hurwitz determinants.[13] Their definition is related to the associated Hurwitz matrix.

Propositions

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Proposition 1. If all the Hurwitz determinants are positive, apart perhaps then the associated Jacobian has no pure imaginary eigenvalues.

Proposition 2. If all Hurwitz determinants (for all in are positive, and then all the eigenvalues of the associated Jacobian have negative real parts except a purely imaginary conjugate pair.

The conditions that we are looking for so that a Hopf bifurcation occurs (see theorem above) for a parametric continuous dynamical system are given by this last proposition.

Example

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Consider the classical Van der Pol oscillator written with ordinary differential equations:

The Jacobian matrix associated to this system follows:

The characteristic polynomial (in ) of the linearization at (0,0) is equal to:

The coefficients are:
The associated Sturm series is:

The Sturm polynomials can be written as (here ):

The above proposition 2 tells that one must have:

Because 1 > 0 and −1 < 0 are obvious, one can conclude that a Hopf bifurcation may occur for Van der Pol oscillator if .

See also

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References

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  1. ^ "Hopf Bifurcations" (PDF). MIT.
  2. ^ Heitmann, S., Breakspear, M (2017-2022) Brain Dynamics Toolbox. bdtoolbox.org doi.org/10.5281/zenodo.5625923
  3. ^ a b Strogatz, Steven H. (1994). Nonlinear Dynamics and Chaos. Addison Wesley. ISBN 978-0-7382-0453-6.
  4. ^ Guckenheimer, J.; Labouriau, J.S. (1993), "Bifurcation of the Hodgkin and Huxley equations: A new twist", Bulletin of Mathematical Biology, 55 (5): 937–952, doi:10.1007/BF02460693, S2CID 189888352.
  5. ^ "Selkov Model Wolfram Demo". [demonstrations.wolfram.com ]. Retrieved 30 September 2012.
  6. ^ López, Álvaro G (2020-12-01). "Stability analysis of the uniform motion of electrodynamic bodies". Physica Scripta. 96 (1): 015506. doi:10.1088/1402-4896/abcad2. ISSN 1402-4896. S2CID 228919333.
  7. ^ Osborne, Andrew G.; Deinert, Mark R. (October 2021). "Stability instability and Hopf bifurcation in fission waves". Cell Reports Physical Science. 2 (10): 100588. Bibcode:2021CRPS....200588O. doi:10.1016/j.xcrp.2021.100588. S2CID 240589650.
  8. ^ For detailed derivation, see Strogatz, Steven H. (1994). Nonlinear Dynamics and Chaos. Addison Wesley. p. 205. ISBN 978-0-7382-0453-6.
  9. ^ Serajian, Reza (2011). "Effects of the bogie and body inertia on the nonlinear wheel-set hunting recognized by the hopf bifurcation theory" (PDF). International Journal of Automotive Engineering. 3 (4): 186–196.
  10. ^ a b 18.385J / 2.036J Nonlinear Dynamics and Chaos Fall 2014: Hopf Bifurcations. MIT OpenCourseWare
  11. ^ Hale, J.; Koçak, H. (1991). Dynamics and Bifurcations. Texts in Applied Mathematics. Vol. 3. Berlin: Springer-Verlag. ISBN 978-3-540-97141-2.
  12. ^ Hairer, E.; Norsett, S. P.; Wanner, G. (1993). Solving Ordinary Differential Equations I: Nonstiff Problems (Second ed.). New York: Springer-Verlag. ISBN 978-3-540-56670-0.
  13. ^ a b Kahoui, M. E.; Weber, A. (2000). "Deciding Hopf bifurcations by quantifier elimination in a software component architecture". Journal of Symbolic Computation. 30 (2): 161–179. doi:10.1006/jsco.1999.0353.

Further reading

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