# Control Example using Matlab

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14 Νοε 2013 (πριν από 4 χρόνια και 6 μήνες)

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Control Example using Matlab

Cruise Control

Modeling a Cruise Control System

The
inertia
of the wheels is
neglected

Aerodynamic Drag

is
neglected

is proportional to the square
of the car’s speed

Physical setup and system equations

The problem is reduced to the simple mass and damper system

It is assumed that
friction

is opposing the motion of the car

is proportional to the car's speed

Modeling a Cruise Control System

Using Newton's law, the dynamic equation
for this system is:

u
b
m
x
x

where u is the force from the engine.

There are several different ways to describe a system of
linear differential equations.

To calculate the Transfer Function, we shall use the Stare
Space representation then transform it to TF using
ss2tf

State
-
space equations

The
state
-
space representation

is given by the
equations:

where

is an n by 1 vector representing the
state

(commonly position and velocity variables in mechanical systems),

u is a scalar representing the
input

(commonly a force or torque in mechanical systems), and

y is a scalar representing the
output
.

Du
C
y
Bu
A
dt
d
x
x
x

x

State Equation for our CC model

The state vector is [ x, ] and y is

x

x

x

The equation

u
m
1
m
b
x
x

is

u
b
m
x
x

or

u
0
x
1
0
y
u
m
1
0
x
m
b
0
1
0
x
x
x
x

Design requirements

For this example, let's assume that

m =
1000
kg

b =
50
Nsec/m

u =
500
N

Design requirements

When the engine gives a
500
Newton force, the car will reach a
maximum velocity of
10
m/s

(
22
mph).

An automobile should be able to accelerate up to that speed in less
than
5
seconds
. Since this is only a cruise control system, a
10
%
overshoot

on the velocity will not do much damage. A
2
-
state error

is also acceptable for the same reason.

Rise time <
5
sec

Overshoot <
10
%

2
%

Matlab representation and open
-
loop response

m =
1000
;

b =
50
;

u =
500
;

A = [
0 1
;
0
-
b/m]

B = [
0
;
1
/m]

C = [
0 1
]

D =
0

step(A,u*B,C,D)

this step response does not meet the design criteria placed on the
problem. The system is
overdamped
, so the overshoot response is
fine, but the
rise time is too slow
.

P controller

The first thing to do in this problem is to
transform

the state
-
space equations into
transfer function

form.

m =
1000
; b =
50
; u =
500
; A = [
0 1
;
0
-
b/m] B = [
0
;
1
/m] C = [
0 1
] D =
0

[num,den]=
ss
2
tf
(A,B,C,D)

Next, close the loop and add some
proportional control

to see
if the response can be improved.

k =
100
;

[numc,denc] = cloop(k*num,den,
-
1
);
%Closed
-
loop transfer
function

t =
0
:
0.1
:
20
;

step
(
10
*numc,denc,t) %for
10
m/sec the signal used by
step

axis
([
0 20 0 10
])

Get the Transfer Function

For

m =
1000
; b =
50
; u =
500
; A = [
0 1
;
0
-
b/m] B = [
0
;
1
/m] C = [
0 1
] D =
0

[num,den]=
ss
2
tf
(A,B,C,D)

Matlab should return the following to the command window:

num =

0 0.0010 0

den =

1.0000 0.0500 0

This is the way Matlab presents the TF

0.001
s +
0

------------------

s^
2
+
0.05
s +
0

Step function

The step function is one of most useful functions in
Matlab for control design.

Given a system that can be described by either a transfer
function or a set of state
-
space equations,
the response
to a step input

can immediately be plotted.

A step input can be described as a
change in the input
from

zero to a finite value at time t =
0
.

By default, the step command performs a unit step (i.e.
the input goes from zero to one at time t =
0
).

The basic command to use the step function is one of the
following

(depending if you have a set of state
-
space equations or a
transfer function form):

step(A,B,C,D)

step(num,den)

P controller

state error is more than 10%,

and
the rise time is still too slow
.

Adjust the proportional gain to make the
response better.

but you will not be able to make
the
steady state value go to 10m/sec

without getting rise times that are too
fast.

You must always keep in mind that you are designing a real system,
and for a cruise control system to respond
0
-
10
m/sec
in less than half
of a second is unrealistic.

To illustrate this idea, adjust the k value to equal
10
,
000
, and you
should get the following velocity response:

P controller

The solution to this problem is to add some
integral control

error.

until you get a reasonable rise time.

For example, with k =
600
, the response should now
look like:

PI controller

k =
600
;

ki =
1
; % small to get feeling for integral response

num
1
= [k ki]; % PI has two gains

den
1
= [
1 0
];

num
2
= conv(num,num
1
); % convolution used to multiply
the

% polynomials representing the
gains

den
2
= conv(den,den
1
);

[numc,denc] = cloop(num
2
,den
2
,
-
1
);

t =
0
:
0.1
:
20
;

step(
10
*numc,denc,t)

axis([
0 20 0 10
])

Remember,
integral control makes the transient response worse
much initially can make the response unrecognizable.

PI controller

the ki value to reduce

With
ki = 40

and
k

more to
800
, the
response looks like
the following:

As you can see, this step
response
meets all of the
design criteria,

and
therefore no more iteration
is needed.

It is also noteworthy that
no derivative control was
needed

in this example

Modeling a Cruise Control System

ךוכיחה חוכ תוחפ עינמה חוכל הווש היצרניאה חוכ
.

Physical setup and system equations

Let’s now add Drag and assume the friction to be non velocity
dependent (the static friction, proportional to normal force).

)
(
)
(
)
(
2
t
Bv
t
Cu
dt
t
dv
M

)
(
)
(
)
(
2
t
u
t
dt
t
d



רמולכ
:

M

-

תינוכמה תסמ

B

-

ךוכיח םדקמ

C

-

תינוכמה לש עונמה חכ

M
CB

B
C
v

max
max
v
v

1
)
(
0

t
u
רשאכו
:

ב תכרעמ תינב

רובע

C =
6250
[N]

B =
2.5
[N/(m/s)
2
]

M =
1250
[Kg]

הרעה
:
ןיב הנתשמ הרקבה תוא
0

ל
-

1
,
ב שמתשהל יאדכ ןכל
-

Saturation

In the Matlab command window write
.

The window that has opened is the

Browser
.

It is used to choose various Simulink modules to use in your simulation.

From this window, choose the
File

, and then
New

(
Model
).

Now we have a blank window, in which we will build our model.

This blank window and the library browser window, will be the windows
we’ll work with.

We choose components from the library browser, and then drag
them to our work window.

We’ll use only the Simulink library (also called toolbox) for now.

As we can see, the Simulink library is divided into several
categories:

1.
Continuous

Provides functions for continuous
time, such as integration, derivative, etc.

2.
Discrete

Provides functions for discrete time.

3.
Funcitons & Tables

Just what the name says.

4.
Math

Simple math functions.

5.
Nonlinear

Several non
-
linear functions, such as
switches, limiters, etc.

6.
Signals & Systems

Components that work
with signals. Pay attention to the
mux/demux
.

7.
Sinks

Components that handle the outputs of the
system (e.g.
display it on the screen
).

8.
Sources

Components that generate source signals
for the system.

Drag the
Constant

component
(
Sources)

to the work window,
and then drag the
Scope

module
(
Sinks)

in the same manner.

Now,
click the little triangle

(output port) to the
right of the
Constant

module
, and
while
holding the mouse button down,
drag the mouse to the

left side of
the scope

(input port) and then
release it.
You should see a
pointed arrow being drawn.

Double click the
Constant

module to open its dialog window.

Now you can change this module’s parameters.

Change the constant value to
5
.

Double
-
click the scope to view its window.

You can choose
Simulation Parameters

from the
Simulation

to change the time

Choose
Start

from the
Simulation

(or press
Ctrl+T
, or click the play button
on the toolbar) to start the simulation.

Choose
Start

from the
Simulation

(or press
Ctrl+T
, or click the play button
on the toolbar) to start the simulation.

Now this is a rather silly simulation. All
it does is output the constant value
5
to the
graph (the x
-
axis represents the simulation
time).

Right
-
click on the scope’s graph window
and choose
Autoscale

to

get the following
result:

Now let’s try something a little
bit more complicated.

First, build the following
system (you can find the
Clock

module in the
Sinks

category
.

The
Trigonometric Function

and
Sum

modules reside in the
Math

category
):

Now, let’s see if the
derivative is really a cosine.

Build the following system
(the
Derivative

module is
located in the
Continuous

category):

We can see that this is indeed a
cosine, but something is wrong
at the beginning.

This is because at time
0
,
the derivative has no
prior information for
calculation

there’s
no initial value

for
the derivative,

so
at the first time step
,
the derivative assumes
that its input has a
constant value (and so the
derivative is
0
).

An other example

Let’s say that we have a differential equation that we want
to model. The equation is:

2
5
.
0
A
A

How can we solve this numerically using Simulink?

5
.
0
0

A
We’ll notice
2
simple facts:

1.
If we have A, then we have A
'

(multiplication).

2.
If we have A
'
, then we have A (integration).

We can get out of this loop by using the initial condition. We know that
A
0

=
0.5
, so now we can calculate A
'

and then recalculate the new
A, and so on.

double
-
click the
Integrator

and choose
Initial Condition Source:
External
.

Note that pressing
ctrl

when clicking the mouse
button on a line,
allows
you to split it into
2
lines
:

Set the simulation
stop time to
3.5
seconds

the solution goes to infinity

and see the results in the scope:

ב תכרעמ תינב

Automatic Cruise Control

רובע

C =
6250
[N]

B =
2.5
[N/(m/s)
2
]

M =
1250
[Kg]

הרעה
:
ןיב הנתשמ הרקבה תוא
0

ל
-

1
,
ב שמתשהל יאדכ ןכל
-

Saturation

Automatic Cruise Control

בכרה לש היצלומיסה תא הנבנ
(plant)

Automatic Cruise Control

Automatic Cruise Control

הלחתהה יאנת רובע

היוצר תוריהמו

,

תכרעמה תוגהנתה תא ןחב

רקב םע
P
,
רקב םע
I

רקב םעו
PI

(
דרפנב הרקמ לכ
.)

הרעה
:
תויהל הרומא היוצר הבוגת
:

-

הריהמ
.

-

לש ילמיסקמ ךרע
(
t
)
הלעי אל
"
ידמ
"
בצמב שרדנה ךרעה לעמ
דימתמ
.

-

איה דימתה בצמה תאיגש
"
ספא
( "
ןתינש לככ הנטק
.)

5
.
0
)
0
(

8
.
0

r

Automatic Cruise Control

P controller

No wind

Automatic Cruise Control

P controller

with wind

Automatic Cruise Control

I controller

No wind

Automatic Cruise Control

PI controller

No wind

Automatic Cruise Control

PI controller

with wind

Automatic Cruise Control

Automatic Cruise Control