Lyapunov stability, mathematics vs reality

In summary, the conversation discusses a situation where the correct way of solving an ordinary Lyapunov problem involves defining a feedback control law u that includes a term for stabilizing the system. However, there is also a mathematically possible solution where u is defined without this term, but it is not a good controller. The reason for this is that the steady state value of u only works when the system state is at the equilibrium point, and adding the extra term helps the solution converge to the equilibrium faster.
  • #1
Liferider
43
0

Homework Statement


I have not been doing Lyapunov for a while and when doing an ordinary Lyapunov problem the other day, I ran into a funny situation.

The correct way of doing it:
\begin{align}
\dot{e} &= \frac{1}{L}(u - R(e + x_{ref})) \\
V(e) &= \frac{1}{2}Le^2 \\
\dot{V} &= Le\dot{e} = Le \left( \frac{1}{L} \left[ u - R(e + x_{ref}) \right] \right) \\
&= - Re^2 + e(u - Rx_{ref})
\end{align}
The system is a modified system, with e defined in terms of the original state x
\begin{equation}
e = x - x_{ref} \ \Rightarrow \ x = e + x_{ref}
\end{equation}
To stabilize the system, we define the feedback control law u to be
\begin{align}
u &= Rx_{ref} - K_pe \\
\Rightarrow \ \dot{V} &= -Re^2 - K_pe^2 < 0
\end{align}

However, mathematically, one could define $u$ to be
\begin{align}
u &= Rx_{ref} \\
\Rightarrow \ \dot{V} &= -Re^2 < 0
\end{align}

I know this will not work, u is not a constant, it is a variable... but still, the mathematics checks out, kind of. What is the best way of explaining why this does not work?
 
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  • #2
Hmm, if I think correctly, then
\begin{equation}
u = Rx_{ref}
\end{equation}
is the steady state value when
\begin{equation}
x=x_{ref}
\end{equation}
Soooo setting u to this value at all times should drive the system state to
\begin{equation}
x_{ref}
\end{equation}
It's just not a very good controller?
 
  • #3
You just add the extra Kp*e to make the solution converge to the equilibrium faster...
 

FAQ: Lyapunov stability, mathematics vs reality

What is Lyapunov stability and how is it related to mathematics and reality?

Lyapunov stability is a concept in mathematics that is used to analyze the behavior of dynamic systems. It is a measure of how a system responds to small changes or disturbances. In regards to mathematics and reality, Lyapunov stability helps us understand the stability of a system and how it may behave in the real world.

How is Lyapunov stability different from other stability concepts?

Lyapunov stability is unique in that it takes into account the behavior of a system over time. Other stability concepts, such as equilibrium stability, only consider the behavior of a system at a specific point in time. Lyapunov stability gives us a more comprehensive understanding of a system's behavior.

How is Lyapunov stability used in practical applications?

Lyapunov stability has many practical applications, especially in engineering and physics. It is used to analyze and design control systems, study the stability of mechanical systems, and understand the stability of electronic circuits. It is also used in economics and biology to model and analyze complex systems.

Can Lyapunov stability be proven in real-world systems?

While Lyapunov stability is a mathematical concept, it can be proven in real-world systems through experimentation and observation. By measuring the response of a system to small disturbances, we can determine its stability and compare it to the predictions of Lyapunov stability analysis.

How does Lyapunov stability account for uncertainties and external disturbances?

Lyapunov stability takes into consideration uncertainties and external disturbances by analyzing the overall behavior of a system over time. It accounts for these factors by looking at the system's response to these disturbances and determining if it remains stable or not. In this way, Lyapunov stability can help us understand how a system will behave in the presence of uncertainties and external disturbances.

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