Lyapunov Stability for a nonlinear system

In summary, the linear systems being discussed are of the form ##\dot{x}(t) = Ax(t)## and their local stability can be inferred from the stability of the linearization, as long as the matrix ##A## does not have eigenvalues on the imaginary axis. The linear system is asymptotically stable if all eigenvalues have negative real parts, and unstable if at least one eigenvalue has a positive real part. The diagram shown is a tool for discussing stability in the special case where ##n = 2##.
  • #1
Arman777
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I am trying to understand attracting, Liapunov stable, asymptotically stable for given coupled system. I don't have any Liapunov function. Just two coupled systems such as

##\dot{x} = y##, ##\dot{y} = -4x##

or sometimes normal systems

##\dot{x} = -x##, ##\dot{y} = -5y##
How can I approach to this problem. Do I have to find the eigenvalues and then eigenvectors, write the solution etc or can it be determined just by looking at the eigenvalues ?

Or is it useful to use this diagram ?
[![enter image description here][1]][1]
I guess it can be determined from
##\lambda_{1,2} = \frac{1}{2} (\tau \pm \sqrt{\tau^2 - 4\Delta})##
##\tau = \lambda_1 + \lambda_2## and ##\Delta = \lambda_1 \lambda_2##

This is a new subject to me so I am kind of confused.
1574177776853.png
 
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  • #2
The linear systems you write down are of the form ##\dot{x}(t) = Ax(t)## for some real, constant ##n \times n## matrix ##A##. Given the title of this topic, I assume that these systems arise as linearizations (at a zero equilibrium, for simplicity) of nonlinear autonomous ODEs.

The basic theorem (sometimes known as the "principle of linearized stability") about this situation says that the local (in)stability of the original, nonlinear system can be inferred from the (in)stability of its linearization, provided that ##A## does not have spectrum (eigenvalues) on the imaginary axis.

In turn, a linear system of the form ##\dot{x}(t) = Ax(t)## is asymptotically stable if all eigenvalues have real parts strictly less than zero, and unstable if there is at least one eigenvalue with real part strictly greater than zero. If there are eigenvalues on the imaginary axis, then conclusions from the linear system do not (in general) carry over to the nonlinear system. (In this case, one may proceed to calculate a higher order "normal form".)

The diagram you show is for linear systems in the particular case ##n = 2##. In this case, you can express the eigenvalue pair (and especially the real parts) in terms of the determinant ##\Delta## and trace ##\tau## of ##A##. Some people find the ##(\Delta,\tau)##-plane a useful aid for stability considerations, but I think it is often easier to consider the eigenvalues directly.
 

1. What is Lyapunov Stability for a nonlinear system?

Lyapunov Stability is a concept in nonlinear control theory that is used to determine the stability of a system. It involves analyzing the behavior of a system over time and determining if it will eventually reach a steady state or oscillate around a certain value.

2. How is Lyapunov Stability different from linear stability?

Lyapunov Stability is different from linear stability because it takes into account the nonlinearities of a system. Linear stability only considers the behavior of a system near its equilibrium point, while Lyapunov Stability looks at the entire trajectory of the system.

3. What is a Lyapunov function?

A Lyapunov function is a mathematical function that is used to analyze the stability of a system. It is a scalar function that decreases or stays constant as the system evolves, and it is typically used to prove the stability of a system.

4. How is Lyapunov Stability used in control systems?

Lyapunov Stability is used in control systems to design controllers that can stabilize a nonlinear system. By analyzing the stability of a system using Lyapunov functions, engineers can design control strategies that ensure the system remains stable and does not deviate from its desired behavior.

5. What are the limitations of Lyapunov Stability?

One limitation of Lyapunov Stability is that it can only determine the stability of a system and cannot guarantee its performance. Additionally, finding a suitable Lyapunov function for a complex system can be challenging and may require a lot of mathematical analysis.

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