How can I prove H(x,y) is a Lyapunov function for this system?

  • Thread starter squenshl
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In summary, the Hamiltonian H(x,y) = y2/2 - x2 + x4 + y2/2 = y2 - x2 + x4 is a Lyapunov function for the system dx/dt = y, dy/dt = 2x - 4x3 - y, as shown by dH/dt < 0. Additionally, H is always positive for nonzero x,y.
  • #1
squenshl
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Consider the system:
dx/dt = y, dy/dt = 2x - 4x3 - y.

I know that the Hamiltonian H(x,y) = y2/2 - x2 + x4 + y2/2 = y2 - x2 + x4. But how do I show that H is a Lyapunov function for this system. Please help.
 
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  • #2


Is it:
d/dt H(x(t),y(t)) = d/dt(y2 - x2 + x4) = y dy/dt + dx/dt(-2x + 4x3) = y(2x - 4x3 - y) + y(-2x + 4x3) = 2xy - 4x3y - y2 - 2xy + 4x3y = -y2 < 0. Since dH/dt < 0, this is a Lyapunov function.
 
  • #3


Also show that H is always positive for nonzero x,y. Then you are done.
 
  • #4


Cool.
Cheers.
 

FAQ: How can I prove H(x,y) is a Lyapunov function for this system?

What does the equation "Dx/dt = y, dy/dt = 2x - 4x" represent?

The equation represents a system of two first-order differential equations, where the rate of change of x with respect to time is equal to y, and the rate of change of y with respect to time is equal to 2x - 4x.

How can this equation be solved?

This equation can be solved using various methods, including separation of variables, substitution, and numerical methods such as Euler's method or the Runge-Kutta method.

What are the applications of this equation?

This type of equation is commonly used in physics, engineering, and other fields to model systems that involve rates of change over time. It can be used to study the behavior of physical systems, such as population growth, chemical reactions, and electrical circuits.

What do the variables in this equation represent?

The variable x typically represents the quantity being studied, while y represents the rate of change of x over time. The variables t and dt represent time and a small change in time, respectively.

What are some possible limitations of this equation?

One limitation of this equation is that it assumes that the rates of change are constant over time, which may not always be the case in real-world systems. Additionally, it may not accurately model complex systems with multiple variables and interactions.

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