Eigenvalues of Linear Time Varying systems

In summary, the conversation discusses the limitations of using usual eigenvalues as a stability analysis tool for LTV systems. The speaker suggests that there may be an extension that involves an inner derivative of the eigenvalues, but is unsure of the specifics. They also mention that non-linear LTV systems, like the example of v^2 drag, can be analyzed using state matrices and that the dynamic behavior of states and their interactions must be considered to ensure stability.
  • #1
sodemus
29
0
The usual eigenvalues of a LTV system does not say much about the stability but my intuition tells me there should be some kind of extension that applies to LTV systems as well. Like including some kind of inner derivative of the eigenvalues or something, I don't know...

I guess in some way part of my question is something like, what invalidates the 'frozen' eigenvalues as a stability analysis tool for LTV systems? What is overlooked?

I can understand that my question can appear a bit fuzzy so please try ask follow-ups!
 
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  • #2
Hard to say for a non-linear LTV in general, but if you consider the simple example of v^2 drag [Vdot = -k/2*V*abs(V)], then you can write the state matrix for a given velocity as...

dVdot = -k/2(abs(V0)+V0*sign(V0))*dV

For V0 > 0 dVdot = -k*V0*dV
For V0 < 0 dVdot = -k*abs(V0)*dV

For this system its very simple to see that for any V0, the eigenvalue is always going to be a negative value with magnitude -k*V0. The pole of this system is neutrally stable when V0 = 0, but it otherwise always located in the stable left half plane.

For an LTV, we simply replace V0 with V, and the same relationship holds. You need to investigate the dynamic behavior of the states as well as their interactions with one another if you want to ensure and LTV system is stable.
 

Related to Eigenvalues of Linear Time Varying systems

1. What are eigenvalues of linear time varying systems?

The eigenvalues of a linear time varying system are the values that make the system's characteristic equation equal to zero. They represent the stability of the system and can be used to analyze the system's behavior over time.

2. How do eigenvalues affect the stability of a linear time varying system?

The eigenvalues determine the stability of the system. If all eigenvalues have negative real parts, the system is stable. If any eigenvalue has a positive real part, the system is unstable. If any eigenvalue has a zero real part, further analysis is needed to determine stability.

3. Can eigenvalues change over time in a linear time varying system?

Yes, the eigenvalues of a linear time varying system can change over time. This is because the system's coefficients, which determine the eigenvalues, can vary with time. As a result, the stability of the system can also change over time.

4. How are eigenvalues calculated for a linear time varying system?

To calculate the eigenvalues of a linear time varying system, the coefficients of the system's characteristic equation must be determined. This can be done by solving a set of differential equations or by using numerical methods such as matrix computations.

5. What is the significance of complex eigenvalues in a linear time varying system?

Complex eigenvalues in a linear time varying system indicate oscillatory behavior, meaning the system will produce sinusoidal outputs. The real part of the complex eigenvalue determines the frequency of the oscillations, while the imaginary part determines the amplitude. Complex eigenvalues can also indicate instability if the real part is positive.

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