Lyapunov stability, mathematics vs reality

AI Thread Summary
The discussion centers on the application of Lyapunov stability in control systems, specifically addressing a scenario where a feedback control law is defined. The correct formulation includes a proportional term (Kp*e) to enhance system convergence to the desired equilibrium state. While mathematically, setting the control input (u) to the steady-state value (Rx_{ref}) appears valid, it fails to account for the dynamic nature of the system, leading to suboptimal performance. The inclusion of the proportional term is essential for achieving faster convergence and effective control. Ultimately, the conversation highlights the importance of considering both mathematical correctness and practical effectiveness in control design.
Liferider
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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?
 
Last edited:
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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?
 
You just add the extra Kp*e to make the solution converge to the equilibrium faster...
 

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