Finding Eigenvectors & Stabilizing 0,0 in System Stability

In summary, the conversation discusses finding eigenvectors and determining the stability of a fixed point in a system with equations \dot{x}=y2 and \dot{y}=x2. It is determined that the fixed point (0,0) is unstable and it is not necessary to compute eigenvectors as the system is Hamiltonian. The Hamiltonian is found by dividing one equation by the other and separating variables, and the level sets of the Hamiltonian define the phase portrait.
  • #1
namu
33
0
For the system

[itex]\dot{x}[/itex]=y2
[itex]\dot{y}[/itex]=x2

Both the eigenvalues are zero. How do I
find the eigenvectors so that I can sketch
the phase portrait and how do I classify
the stability of the fixed point (0,0)?
 
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  • #2
Well, obviously, both [itex]x^2[/itex] and [itex]y^2[/itex] are positive for all non-zero x and y so (0, 0) is unstable.
 
  • #3
Yes, that is true. Thank you. How do I find the eigenvectors though?
 
  • #4
It is not necessary to compute eigenvectors. This system is Hamiltonian (conservative). On dividing one equation by the other you get
\begin{equation}
\frac{dx}{dy} = \frac{y^2}{x^2}
\end{equation}
Separating variables and integrating you find the Hamiltonian
\begin{equation}
H(x,y) = \frac{1}{3} (x^3-y^3)
\end{equation}
The level sets \begin{equation}H = constant\end{equation} define the phase portrait.
 
  • #5
oh my god, that make life so easy. Thank you!
 

FAQ: Finding Eigenvectors & Stabilizing 0,0 in System Stability

What are eigenvectors and why are they important in system stability?

Eigenvectors are special vectors that represent the directions in which a linear transformation or matrix acts by simply scaling the vector. In the context of system stability, eigenvectors are important because they represent the directions in which a system's state will change over time, and their corresponding eigenvalues determine the rate at which these changes occur.

What is the process for finding eigenvectors in a system stability analysis?

The process for finding eigenvectors in a system stability analysis involves solving the characteristic equation of the system, which is a mathematical equation that relates the eigenvalues and eigenvectors of a matrix. This equation can be solved using various techniques such as Gaussian elimination or the power method. Once the eigenvalues are found, the corresponding eigenvectors can be calculated by plugging them into the characteristic equation and solving for the unknown variables.

How can eigenvectors be used to stabilize the (0,0) point in a system?

The (0,0) point, also known as the equilibrium point, represents the state where the system is in a steady state with no changes occurring. In order to stabilize this point, the eigenvalues of the system need to have negative real parts. This means that the system's state will converge towards the (0,0) point over time, making it a stable equilibrium. By finding the eigenvectors of the system and manipulating the system's parameters, it is possible to make the eigenvalues have negative real parts and thus stabilize the (0,0) point.

What are the benefits of stabilizing the (0,0) point in a system?

Stabilizing the (0,0) point in a system has several benefits. Firstly, it ensures that the system will not experience any unexpected and potentially damaging changes in its state. This is particularly important in systems that are used in critical applications such as aerospace or medical devices. Additionally, stabilizing the (0,0) point can also improve the overall performance and efficiency of the system by minimizing any undesired oscillations or deviations from the desired state.

Are there any limitations to using eigenvectors to stabilize the (0,0) point in a system?

While eigenvectors are a powerful tool for stabilizing the (0,0) point in a system, they do have some limitations. One limitation is that they can only be used for linear systems, meaning that the relationship between the system's inputs and outputs is linear. Additionally, in some cases, it may not be possible to manipulate the system's parameters to achieve the desired eigenvalues, making it difficult to stabilize the (0,0) point. In these cases, alternative methods may need to be used for system stability analysis.

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