In this section
|
**Non--Linear Systems are often approximated by Linear Systems (locally).** |
|
" |
|
|
|
# ╔═╡ e860d92b-cc8f-479b-a0fc-e5f7a11ae1fd |
|
Foldable(md" $\dot{x} = f(x,u) \; \implies \; A=? \; B=?$", md""" |
|
|
|
Suppose now that we apply our dynamics equation to an input: |
|
|
|
```math |
|
u(t) = u_{eq} + \delta u(t), \quad t \ge 0 |
|
``` |
|
where $u_{eq}$ is an fixed input and $\delta u(t)$ is a perturbation function such that the input is close |
|
but not equal to $u_{eq}$ and similarly we perturb the initial condition: |
|
|
|
```math |
|
x(0) = x_e + \delta x(0) |
|
``` |
|
|
|
We will define the deviation from the reference state as: |
|
```math |
|
\delta x(t) = x(t) - x_e, \quad t \ge 0 |
|
``` |
|
|
|
To determine the evolution of $\delta x(t)$, we can expand the dynamics around the reference point using a Taylor expansion: |
|
|
|
```math |
|
\dot{\delta x}(t) = f(x_e + \delta x(t), u_{eq} + \delta u(t)) |
|
``` |
|
```math |
|
=\frac{\partial f}{\partial x}\bigg|_{(x_e, u_{eq})} \delta x(t) + \frac{\partial f}{\partial u}\bigg|_{(x_e, u_{eq})} \delta u(t) + \mathcal{O}(\|\delta x\|^2) + \mathcal{O}(\|\delta u\|^2) |
|
``` |
|
|
|
Considering just the first-order terms we obtain: |
|
|
|
```math |
|
A= \frac{\partial f}{\partial x}|_{(x_e,u_e)} |
|
, \quad B= \frac{\partial f}{\partial u}|_{(x_e,u_e)} |
|
``` |
|
|
|
**Attention!** The linearization describes perturbations around the reference $(x_e,u_e)$; it is valid only while $\|\delta x\|$ and $\|\delta u\|$ remain small. |
|
|
|
""") |
There appears to be a notation
$u_{eq}$ while in the final formulae it seems to be come
$u_e$.
$u_{eq}$ could be renamed as
$u_e$ given that there is also an
$x_e$, or they could be replaced with
$u_{eq}$ and
$x_{eq}$.
In this section
LearningToControlClass/class01/class01_intro.jl
Lines 834 to 875 in 507e0b2
There appears to be a notation