CONTROL SYSTEMS

LAB

II YEAR II SEM

Department of Electrical and Electronics

Engineering
Gokaraju Rangaraju Institute of Engineering & Technology
Bachupally ,Kukatpally , Hyderabad
Telangana State - 500 090

i
 GOKARAJU RANGARAJU INSTITUTE OF
ENGINEERING AND TECHNOLOGY

(Autonomous Institute under JNTU Hyderabad)

CERTIFICATE

This is to certify that it is a bonafide record of practical work done in

the Control systems Laboratory in II semester of II year during the
year 2014-2015

Name:
Roll No:
Branch: Signature of staff member

ii
Contents:
S.No Name of the Experiment Page Date Signature
No.
1. TRANSFER FUNCTION FROM
ZEROS AND POLES

2. ZEROS AND POLES FROM

TRANSFER FUNCTION

3. STEP RESPONSE OF A
TRANSFER FUNCTION

4. IMPULSE RESPONSE OF A
TRANSFER FUNCTION

5. RAMP RESPONSE OF A
TRANSFER FUNCTION

6. TIME RESPONSE OF A SECOND

ORDER SYSTEM

7. TRANSFER FUNCTION OF A D.C

MOTOR

8. ROOT LOCUS FROM A

TRANSFER FUNCTION

9. BODE PLOT FROM A TRANSFER

FUNCTION

10. TRANSFER FUNCTION FROM

STATE MODEL

11. STATE MODEL FROM

TRANSFER FUNCTION

12. STATE FROM ZEROES AND

POLES

13. ZEROS AND POLES FROM

STATE MODEL

14. STEP RESPONSE OF A STATE

MODEL

15. IMPULSE RESPONSE OF A

STATE MODEL

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16. RAMP RESPONSE OF A STATE
MODEL

17. PID CONTROLLER

18. LAG COMPENSATOR

19. LEAD COMPENSATOR

20. LEAD-LAG COMPENSATOR

21. NYQUIST PLOT FROM A

TRANSFER FUNCTION

22. SYSTEM IDENTIFICATION OF

D.C.MOTOR USING MATLAB
&LABVIEW

23. REALIAZATION OF TRANSFER

FUNCTION USING OP-AMPS

24. COMPENSATOR DESIGN FOR A

LOW PASS FILTER AND
REALIZE USING OP-AMP
CIRCUIT

25. DISCRETIZE THE

COMPENSATOR TRANSFER
FUNCTION

26. SIMULATION OF A CLOSED

LOOP SYSTEM(PLANT AND
COMPENSATOR)

27. HARDWARE REALIZATION &

IMPLEMENTATION OF CLOSED
LOOP SYSTEM USING ARDUINO
AND MATLAB

iv
v
vi
 1 .TRANSFER FUNCTION FROM ZEROS AND POLES
AIM:
To obtain a transfer function from given poles and zeroes using MATLAB

APPARATUS:
Software: MATLAB

THEORY:

A transfer function is also known as the network function is a mathematical representation, in

terms of spatial or temporal frequency, of the relation between the input and output of a (linear
time invariant) system. The transfer function is the ratio of the output Laplace Transform to the
input Laplace Transform assuming zero initial conditions. Many important characteristics of
dynamic or control systems can be determined from the transfer function.

The transfer function is commonly used in the analysis of single-input single-output electronic
system, for instance. It is mainly used in signal processing, communication theory, and control
theory. The term is often used exclusively to refer to linear time-invariant systems (LTI). In its
simplest form for continuous time input signal x(t) and output y(t), the transfer function is the
linear mapping of the Laplace transform of the input, X(s), to the output Y(s).

Zeros are the value(s) for z where the numerator of the transfer function equals zero. The
complex frequencies that make the overall gain of the filter transfer function zero. Poles are the
value(s) for z where the denominator of the transfer function equals zero. The complex
frequencies that make the overall gain of the filter transfer function infinite.

The general procedure to find the transfer function of a linear differential equation from input to
output is to take the Laplace Transforms of both sides assuming zero conditions, and to solve for
the ratio of the output Laplace over the input Laplace.

MATLAB PROGRAM:
z=input(enter zeroes)
p=input(enter poles)
k=input(enter gain)

1
[num,den]=zp2tf(z,p,k)
tf(num,den)

PROCEDURE:

Write MATLAB program in the MATLAB editor document.

Then save and run the program.
Give the required input.
The syntax zp2tf(z,p,k) and tf(num,den) solves the given input poles and zeros and
gives the transfer function.
zp2tf forms transfer function polynomials from the zeros, poles, and gains of a system in
factored form.
Now find the output theoretically for the given transfer function and compare it with the output
obtained practically

EXAMPLE:
Given poles are -3.2+j7.8,-3.2-j7.8,-4.1+j5.9,-4.1-j5.9,-8 and the zeroes are -0.8+j0.43,-0.8-
j0.43,-0.6 with a gain of 0.5

THEORITICAL CALCULATIONS:
Enter zeros
Z=

Enter poles
P=

Enter gain
K=

2
num =
den =

Transfer function=

RESULT:

3
 2.ZEROS AND POLES FROM TRANSFER FUNCTION

AIM:
To obtain zeros and poles from a given transfer function using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
The transfer function provides a basis for determining important system
response characteristics without solving the complete differential equation. As defined, the

transfer function is a rational function in the complex variable that is

It is often convenient to factor the polynomials in the numerator and the

denominator, and to write the transfer function in terms of those factors:

where, the numerator and denominator polynomials, N(s) and

D(s), have real coefficients defined by the systems differential equation and .

As written in the above equation,

the are the roots of the equation and are defined to be the system zeros

the are the roots of the equation and are defined to be the system poles.

4
MATLAB PROGRAM:
num = input(enter the numerator of the transfer function)

den = input(enter the denominator of the transfer function)

[z,p,k] = tf2zp(num,den)

EXAMPLE:
Obtain the poles and zeros of the transfer function given below:

PROCEDURE:

Type the program in the MATLAB editor that is in M-file.

Save and run the program.
Give the required inputs in the command window of MATLAB in matrix format.
tf2zp converts the transfer function filter parameters to pole-zero-gain form.
[z,p,k] = tf2zp(b,a) finds the matrix of zeros z, the vector of poles p, and the associated
vector of gains k from the transfer function parameters b and a:
The numerator polynomials are represented as columns of the matrix b.
The denominator polynomial is represented in the vector a.
Note down the output of the program that is zeros, poles and gain obtained in MATLAB.
The zeros, poles and gain are also obtained theoretically.

THEORITICAL CALCULATIONS:
Enter the numerator of the transfer function
num =

Enter the denominator of the transfer function

den =

5
z=

p=

RESULT:

6
 3.STEP RESPONSE OF A TRANSFER FUNCTION
AIM:
To obtain the step response of a transfer function of the given system using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
A step signal is a signal whose value changes from one level to another level in zero time.
Mathematically, the step signal is represented as given below:

where

In the Laplace transform form,

The step response of the given transfer function is obtained as follows:

So,

The output is given by,

MATLAB PROGRAM:
num = input(enter the numerator of the transfer function)
7
den = input(enter the denominator of the transfer function)
step(num,den)

EXAMPLE:
Obtain the step response of the transfer function given below:

PROCEDURE:

Type the program in MATLAB editor that is in M-file.

Save and run the program.
Give the required inputs in the command window of MATLAB in matrix format.
step function calculates the unit step response of a linear system.
Zero initial state is assumed in state-space case.
When invoked with no output arguments, this function plots the step response on the
screen.
step (sys) plots the response of an arbitrary LTI system.
This model can be continuous or discrete, and SISO or MIMO.
The step response of multi-input systems is the collection of step responses for each
input channel.
The duration of simulation is determined automatically based on the system poles
and zeroes.
Note down the response of the transfer function obtained in MATLAB.
The response of the transfer function is also obtained theoretically.
Both the responses are compared.

THEORETICAL CALCULATIONS:

8
GRAPH:

9
TABULAR FORM

T C(T)
0

RESULT:

10
 4.IMPULSE RESPONSE OF A TRANSFER FUNCTION
AIM:
To obtain the impulse response of a transfer function of the given system using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
An impulse signal is a signal whose value changes from zero to infinity in zero time.
Mathematically, the unit impulse signal is represented as given below:

where:

In the Laplace transform form,

The impulse response of the given transfer function is obtained as follows:

So,

The output is given by,

MATLAB PROGRAM:
num = input(enter the numerator of the transfer function)
11
den = input(enter the denominator of the transfer function)
impulse(num,den)

EXAMPLE:
Obtain the impulse response of the transfer function given below:

PROCEDURE:

Type the program in the MATLAB editor that is in M-file.

Save and run the program.
Give the required inputs in the command window of MATLAB in matrix format.
impulse calculates the impulse response of a linear system.
The impulse response is the response to the Dirac input, (t) for continuous time systems
and to a unit pulse at for discrete time systems.
Zero initial state is assumed in the state space case.
When invoked without left hand arguments, this function plots the impulse response on
the screen.
impulse(sys) plots the impulse response of an arbitrary LTI model sys.
This model can be continuous or discrete, SISO or MIMO.
The impulse response of multi-input systems is the collection of impulse responses for
each input channel.
The duration of simulation is determined automatically to display the transient behavior
of the response.
Note down the response of the given transfer function obtained in MATLAB.
The response of the transfer function is also obtained theoretically.
Both the responses are compared.

THEORETICAL CALCULATIONS:

12
GRAPH

13
TABULAR FORM:

T C(t)
0

RESULT:

5.RAMP RESPONSE OF A TRANSFER FUNCTION

14
AIM:
To obtain the ramp response of a transfer function of the given system using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
A ramp signal is a signal which changes with time gradually in a linear fashion. Mathematically,
the unit ramp signal is represented as given below:

In the Laplace transform form,

The step response of the given transfer function is obtained as follows:

So,

The output is given by,

15
MATLAB PROGRAM:

num = input(enter the numerator of the transfer function)

den = input(enter the denominator of the transfer function)

lsim(num,den,u,t)

EXAMPLE:
Obtain the ramp response of the transfer function given below:

PROCEDURE:

Type the program in the MATLAB editor that is in M-file.

Save and run the program.
Give the required inputs in the command window of MATLAB in matrix format.
lsim simulates the (time) response of continuous or discrete linear systems to arbitrary
inputs.
When invoked without left-hand arguments, lsim plots the response on the screen.
lsim(sys,u,t) produces a plot of the time response of the LTI model sys to the input time
history t,u.
The vector t specifies the time samples for the simulation and consists of regularly spaced
time samples.
t = 0:dt:Tfinal
The matrix u must have as many rows as time samples (length(t)) and as many columns
as system inputs.
Each row u(i,:) specifies the input value(s) at the time sample t(i).
Note down the response of the transfer function obtained in MATLAB.
The response of the transfer function is also obtained theoretically.
Both the responses are compared.

THEORETICAL CALCULATIONS:

16
17
TABULAR FORM:

T C(t)
0

RESULT:

6.TIME RESPONSE OF SECOND ORDER SYSTEM

AIM:
To obtain the time response of a given second order system with its damping frequency.
18
APPARATUS:
Software: MATLAB

THEORY:
The time response has utmost importance for the design and analysis of control systems because
these are inherently time domain systems where time is independent variable. During the
analysis of response, the variation of output with respect to time can be studied and it is known
as time response. To obtain satisfactory performance of the system with respect to time must be
within the specified limits. From time response analysis and corresponding results, the stability
of system, accuracy of system and complete evaluation can be studied easily.
Due to the application of an excitation to a system, the response of the system is known as time
response and it is a function of time. The two parts of response of any system:
(i) Transient response
(ii) Steady-state response.
Transient response: The part of the time response which goes to zero after large interval of time
is known as transient response.
Steady state response: The part of response that means even after the transients have died out is
said to be steady state response.
The total response of a system is sum of transient response and steady state response:
C(t)=Ctr(t)+Css(t)

TIME RESPONSE OF SECOND ORDER CONTROL SYSTEM:

A second order control system is one wherein the highest power of s in the denominator of its
transfer function equals 2.
Transfer function is given by:

TF=

nis called natural frequency of oscillations.

d=n is called damping frequency oscillations.

affects damping and called damping ratio.

19
 is called damping factor or actual damping or damping coeeficient.

MATLAB PROGRAM:
wn=input('enter value of undamped natural frequency')
z=input('enter value of damping ratio')
n=[wn*wn]
p=sqrt(1-z^2)
wd=wn*p

h=[p/z]
k=atan(h)
m=pi-k;
tr=[m/wd]
tp=[pi/wd]
q=z*wn

ts=[h/q]
r=z*pi
f=[r/p]
mp=exp(-f)
num=[0 0 n]
den=[1 2*z*wn n]

s=tf(num,den)
hold on
step(s)
impulse(s)
hold off

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PROCEDURE:
1.Time response of the system is being found when we give the values of natural undamped
frequency and damping ratio.
2.When we give these values first rise time ,peak time,peak overshoot,transfer function are being
calculated.
3.Then step(s) And impulse(s) generates time response of the system.
5.The hold function determines whether new graphics object are added to the graph or replaces
objects in the graph.
6.hold on retains the current plot and certain axes properties so that subsequent graphing
command add to the existing graph.

7.hold off resets axes properties to their defaults before drawing new plots.hold off is the default.

THEORETICAL CALCULATIONS:

GRAPH:

21
RESULT:

22
 7.TRANSFER FUNCTION OF A DC MOTOR
AIM:

To determine the transfer function of a DC Motor.

Interaction between mechanical and electrical quantities of a motor.
Measuring time response of a DC motor and comparing with time response obtained
through transfer function.

THEORY:
The transfer function of a DC motor is studied ( in general)

23
PROCEDURE:
Type the program in the MATLAB editor that is in M-file.
Save and run the program.
Give the required inputs in the command window of MATLAB .

MATLAB PROGRAM:
J=0.01;
B=0.1;
K=0.01;
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 R=1;
L=0.5;
S=tf(s);
P_motor=K/((J*s+B)*(Ls+R)+K^2);
zpk(P_motor);

THEORITICAL CALCULATIONS:

RESULT:

25
 8.ROOT LOCUS FROM A TRANSFER FUNCTION
AIM:
To plot the root locus for a given transfer function of the system using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
rlocus computes the Evans root locus of a SISO open-loop model. The root locus gives the
closed-loop pole trajectories as a function of the feedback gain k (assuming negative feedback).
Root loci are used to study the effects of varying feedback gains on closed-loop pole locations.
In turn, these locations provide indirect information on the time and frequency responses.

rlocus(sys) calculates and plots the rootlocus of the open-loop SISO model sys. This function can
be applied to any of the following feedback loops by setting sys appropriately.
If sys has transfer function

h(s) =

The closed-loop poles are the roots of

d(s) + k*n(s)=0

MATLAB PROGRAM:
num=input(enter the numerator of the transfer function)
den=input(enter the denominator of the transfer function)
h=tf(num,den)

rlocus(h)

PROCEDURE:

Write MATLAB program in the MATLAB specified documents.

Then save the program to run it.
26
 The input is to be mentioned.
The syntax h=tf(num,den) gives the transfer function and is represented as h.
The syntax rlocus(h) plots the rootlocus of the transfer function h.
Generally the syntax is of the form
rlocus(sys)
rlocus(sys,k)

rlocus(sys1, sys2, .)

[r,k] = rlocus(sys)
r = rlocus(sys,k)

rlocus(sys) calculates and plots the root locus of the open loop SISO model sys.
Now we have to solve it theoretically.
Now we have to compare the practical and theoretical ouputs to verify each other
correctly.

EXAMPLE:

Transfer function =

THEORETICAL CALCULATIONS:

enter the numerator of the transfer function

num=
enter the denominator of the transfer function
den=

Transfer function :

27
RESULT:

28
 9.BODE PLOT FROM A TRANSFER FUNCTION
AIM:
To obtain bode plot for a givan transfer function of the system using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
Bode computes the magnitude and phase of the frequency response of LTI models. When
invoked without left-side arguments, bode produces a Bode plot on the screen. The magnitude is
plotted in decibels (dB), and the phase in degrees. The decibel calculation for mag is computed
as 20log10(|H(jw)|), where H(jw) is the system's frequency response. Bode plots are used to
analyze system properties such as the gain margin, phase margin, DC gain, bandwidth,
disturbance rejection, and stability.

Bode Calculations Gain

The magnitude of the transfer function T is defined as:

However, it is frequently difficult to transition a function that is in "numerator/denominator"

form to "real+imaginary" form. Luckily, our decibel calculation comes in handy. Let's say we
have a frequency response defined as a fraction with numerator and denominator polynomials
defined as:

If we convert both sides to decibels, the logarithms from the decibel calculations convert
multiplication of the arguments into additions, and the divisions into subtractions:

Gain = 20log(j + zn) 20log(j + pm)

n m

29
bode(sys) plots the Bode response of an arbitrary LTI model sys. This model can be continuous
or discrete, and SISO or MIMO. In the MIMO case, bode produces an array of Bode plots, each
plot showing the Bode response of one particular I/O channel. The frequency range is
determined automatically based on the system poles and zeros.
bode(sys,w) explicitly specifies the frequency range or frequency points to be used for the plot.
To focus on a particular frequency interval [wmin,wmax], set w = {wmin,wmax}. To use
particular frequency points, set w to the vector of desired frequencies. Use logspace to generate
logarithmically spaced frequency vectors. All frequencies should be specified in radians/sec.

bode(sys1,sys2,...,sysN) or bode(sys1,sys2,...,sysN,w) plots the Bode responses of several LTI

models on a single figure. All systems must have the same number of inputs and outputs, but
may otherwise be a mix of continuous and discrete systems. This syntax is useful to compare the
Bode responses of multiple systems.
bode(sys1,'PlotStyle1',...,sysN,'PlotStyleN') specifies which color, linestyle, and/or marker
should be used to plot each system. For example,
bode(sys1,'r--',sys2,'gx')
uses red dashed lines for the first system sys1 and green 'x' markers for the second system sys2.
When invoked with left-side arguments
[mag,phase,w] = bode(sys)
[mag,phase] = bode(sys,w)

return the magnitude and phase (in degrees) of the frequency response at the frequencies w (in
rad/sec). The outputs mag and phase are 3-D arrays with the frequency as the last dimension (see
"Arguments" below for details). You can convert the magnitude to decibels by
magdb = 20*log10(mag)

MATLAB PROGRAM:
num=input('enter the numerator of the transfer function')

den=input('enter the denominator of the transfer function')

h=tf(num,den)
[gm pm wcp wcg]=margin(h)
bode(h)

30
PROCEDURE:

Write the MATLAB program in the MATLAB editor.

Then save and run the program.
Give the required inputs.
The syntax "bode(h)" solves the given input transfer function and gives the bode plot,
where num,den are the numerator and denominator of the transfer function.
Now plot the bode plot theoretically for the given transfer function and compare it with the
plot obtained practically.

EXAMPLE:
Transfer function=

THEORETICAL CALCULATIONS:
enter the numerator of the transfer function

num =

enter the denominator of the transfer function

den =

Transfer function:

gm =

pm =
wcp =
wcg =

31
GRAPH

32
RESULT:

33
 10.TRANSFER FUNCTION FROM STATE MODEL

AIM:
To obtain the transfer function from the state model.

APPARATUS:
Software: MATLAB

THEORY:
The transfer function is defined as the ratio of Laplace transform of output to
Laplace transform of input. The transfer function of a given state model is given by:

A state space representation is a mathematical model of a physical system as a

set of input, output and state variables related by first-order differential equations. The state
space representation (also known as the "time-domain approach") provides a convenient and
compact way to model and analyze systems with multiple inputs and outputs.
Unlike the frequency domain approach, the use of the state space
representation is not limited to systems with linear components and zero initial conditions.

"State space" refers to the space whose axes are the state variables. The state of the system
can be represented as a vector within that space.
The input state equation is given by,

The output equation is written as,

MATLAB PROGRAM:
A =input(enter the matrix A)

34
B= input(enter the matrix B)
C = input(enter the matrix C)

D= input(enter the matrix D)

Sys =ss2tf(A,B,C,D)

EXAMPLE:
Obtain the transfer function from the State Model given below:

A=

B=

C=

D=

PROCEDURE:

Type the program in the MATLAB editor that is in M-file.

Save and run the program.
Give the required inputs in the command window of MATLAB in matrix format.
The command ss2tf(A,B,C,D)) converts the given transfer function into a state model.
Note down the output obtained in MATLAB.
The Transfer Function is also obtained theoretically.
Both the state models are compared.

35
THEORETICAL CALCULATIONS:

A=

B=

C=

D=

RESULT:

36
 11.STATE MODEL FROM TRANSFER FUNCTION

AIM:
To obtain the state model from the given transfer function.

APPARATUS:
Software: MATLAB

THEORY:
There are three methods for obtaining state model from transfer function:
1. Phase variable method

2. Physical variable method

3. Canonical variable method

Out of three methods given above canonical form is probably the most straightforward method
for converting from the transfer function of a system to a state space model is to generate a
model in "controllable canonical form." This term comes from Control Theory but its exact
meaning is not important to us. To see how this method of generating a state space model
works, consider the third order differential transfer function:

We start by multiplying by Z(s)/Z(s) and then solving for Y(s) and U(s) in terms of Z(s). We
also convert back to a differential equation.

We can now choose z and its first two derivatives as our state variables

37
Now we just need to form the output

From these results we can easily form the state space model:

In this case, the order of the numerator of the transfer function was less than that of the
denominator. If they are equal, the process is somewhat more complex. A result that
works in all cases is given below; the details are here. For a general nth order transfer
function:

the controllable canonical state space model form is

MATLAB PROGRAM:
num=input(enter the numerator of the transfer function)
den=input(enter the denominator of the transfer function)
ss(tf(num,den))
38
EXAMPLE:
Obtain the state model from the transfer function given below:

PROCEDURE:

Type the program in the MATLAB editor that is in M-file.

Save and run the program.
Give the required inputs in the command window of MATLAB in matrix format.
The command ss(tf(num,den)) converts the given transfer function into a state model.
Note down the output obtained in MATLAB.
The state model is also obtained theoretically.
Both the state models are compared.

THEORETICAL CALCULATIONS
Enter the transfer function

39
A=

B=

C=

D=

RESULT:

40
 12.STATE MODEL FROM ZEROS AND POLES

AIM:
To obtain a state model from given poles and zeros using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
Lets say we have a transfer function defined as a ratio of two polynomials:

H(s)=

Where N(s) and D(s) are simple polynomials.

Zeroes are the roots of N(s) (the numerator of the transfer function)obtained by setting N(s)=0
and solving for s. Poles are the roots of D(s) (the denominator of the transfer function),obtained
by setting D(s)=0 and solving for s.
The state space model represents a physical system as n first order coupled differential equations.
This form is better suited for computer simulation than an nth order input-output differential
equation.

The general vector-matrix form of state space model is:

Where,
X = state vector

U = input vector
A = n x n matrix
B = n x 1 matrix
The output equation for the above system is,

41
MATLAB PROGRAM:
z=input('enter zeros')

p=input('enter poles')

k=input('enter gain')

[A,B,C,D]=zp2ss(z,p,k)

PROCEDURE:

Open the MATLAB window and open a new MATLAB editor.

Write the MATLAB program in the MATLAB editor.
Save and run the MATLAB program.
Enter the given poles, zeros and gain as input in matrix format.
The syntax [A,B,C,D]=zp2ss(z,p,k) solves zeroes, poles and gain given in the
matrix format as input and gives the output in the form of a state model.
This syntax transforms the given zeros, poles and gain into a state model.
Note down the output state model obtained practically by using the syntax
[A,B,C,D]=zp2ss(z,p,k) .
Now find the state model theoretically for the given poles, zeros and gain.
Compare the theoretically obtained state model from the given poles, zeros and
gain with the one obtained practically. Write the result based on the comparison
between thoretical and practical result.

EXAMPLE:
zeros are:

poles are:

gain=

42
THEORETICAL CALCULATIONS:

Given,zeros =

Given, poles=

X(s)=

X(t)=

Let ;

Y(s)=
Y(t)=

Therefore,
A=

B=

43
C=

D=

RESULT:

Theoretical Result:

Practical Result:

44
 13.ZEROS AND POLES FROM STATE MODEL

AIM:
To obtain poles and zeros from a given state model using MATLAB

APPARATUS:
Software: MATLAB

THEORY:
Lets say we have a transfer function defined as a ratio of two polynomials:

H(s)=

Where N(s) and D(s) are simple polynomials.

Zeroes are the roots of N(s) (the numerator of the transfer function)obtained by setting N(s)=0
and solving for s. Poles are the roots of D(s) (the denominator of the transfer function),obtained
by setting D(s)=0 and solving for s.
The state space model represents a physical system as n first order coupled differential equations.
This form is better suited for computer simulation than an nth order input-output differential
equation.
The general vector-matrix form of state space model is:

Where,
X = state vector
U = input vector

A = n x n matrix
B = n x 1 matrix
The output equation for the above system is,

45
MATLAB PROGRAM:
A=input('enter matrix A')

B=input('enter matrix B')

C=input('enter matrix C')

D=input('enter matrix D')

[z,p,k]=ss2zp(A,B,C,D)

EXAMPLE:

A=

B=

C=

D=

PROCEDURE:

the MATLAB window and open a new MATLAB editor.

Write the MATLAB program in the MATLAB editor.

Open Save and run the MATLAB program.

The state model is given as input and entered in matrix format.
The syntax [z,p,k]=ss2zp(A,B,C,D) solves the given state model entered in matrix
format as input and gives the output in the form of poles, zeros and gain.
This syntax transforms the given state model into poles, zeros and gain.

46
 Note down the output zeros, poles and gain obtained practically by using the syntax
[z,p,k]=ss2zp(A,B,C,D).
Now find the poles, zeros and gain theoretically for the given state model
Compare the theoretically obtained poles, zeros and gain from the given state model with
the one obtained practically. Write the result based on the comparison between
theoretical and practical result.

THEORETICAL CALCULATIONS:

RESULT:
Practical Result:

Theoretical Result:

47
 14. STEP RESPONSE OF A STATE MODEL
AIM:
To find the step response of a state model for a given system using MATLAB.

APPARATUS:
Software: MATLAB

THEORY:
A step signal is a signal whose value changes from one level to another level in zero time.
Mathematically, the step signal is represented as given below:
where

In the Laplace transform form,

The step response of the given transfer function is obtained as follows:

1. The state model is first converted into a transfer function.
2. Step response for that transfer function is obtained as follows.

So,

The output is given by,

48
MATLAB PROGRAM:
A=input(enter matrix A)
B=input(enter matrix B)
C=input(enter matrix C)
D=input(enter matrix D)
Step(A,B,C,D)

EXAMPLE:
Obtain the step response of the state model for the given system.

T.F.=

PROCEDURE:

Step calculates the unit step response of a linear system. Zero initial state is assumed in
the state-space case.
When invoked with no output arguments, this function plots the step response on the
screen.
Step(sys) plots the step response of an arbitrary LTI model sys. This model can be
continuous or discrete, and SISO or MIMO.
The duration of simulation is determined automatically based on the system poles and
zeros.
You can specify either a final time t = Tfinal (in seconds), or a vector of evenly spaced
time samples of the form
t = 0:dt:Tfinal

For discrete systems, the spacing dt should match the sample period. For continuous
systems, dt becomes the sample time of the discretized simulation model (see
"Algorithm"), so make sure to choose dt small enough to capture transient phenomena.
To plot the step responses of several LTI models sys1,..., sysN on a single figure, use
step(sys1,sys2,...,sysN)

49
step(sys1,sys2,...,sysN,t)All systems must have the same number of inputs and outputs but may
otherwise be a mix of continuous- and discrete-time systems.

This syntax is useful to compare the step responses of multiple systems

GRAPH:

50
THEORETICAL CALCULATIONS

RESULT:

51
 15.IMPULSE REPONSE OF A STATE MODEL
AIM:
To obtain the impulse response of a state model for a given system.

APPARATUS:
Software: MATLAB

THEORY:
In the time domain, we generally denote the input to a system as x(t), and the output of the
system as y(t). The relationship between the input and the output is
denoted as the impulse response, h(t).
We define the impulse response as being the relationship between the system output to it's input.
We can use the following equation to define the impulse response:

Impulse Function
It would be handy at this point to define precisely what an "impulse" is. The Impulse Function,
denoted with (t) is a special function defined piece-wise as follows:

An examination of the impulse function will show that it is related to the unit-step function as
follows:

And

The impulse function is not defined at point t = 0, but the impulse response must always satisfy
the following condition, or else it is not a true impulse function:

52
The response of a system to an impulse input is called the impulse response.
Now, to get the Laplace Transform of the impulse function, we take the derivative of the unit
step function

MATLAB PROGRAM:

A=input('enter matrix A')

B=input('enter matrix B')
C=input('enter matrix C')
D=input('enter matrix D')
impulse(A,B,C,D)

PROCEDURE:
1. Impulse calculates the unit impulse response of a linear system.
2. Zero initial state is assumed in the state-space case. When invoked without left-hand
arguments, this function plots the impulse response on the screen.
3. Impulse(sys) plots the impulse response of an arbitrary LTI model sys. This model can be
continuous or discrete, and SISO or MIMO.
4. The duration of simulation is determined automatically to display the transient behavior
of the response.
5. Impulse(sys,t) sets the simulation horizon explicitly. You can specify either a final time t
= Tfinal (in seconds), or a vector of evenly spaced time samples of the formt = 0:dt:Tfinal
6. For discrete systems, the spacing dt should match the sample period. For continuous
systems, dt becomes the sample time of the discretized simulation model (see
"Algorithm"), so make sure to choose dt small enough to capture transient phenomena.
7. To plot the impulse responses of several LTI models sys1,..., sysN on a single figure, use
impulse(sys1,sys2,...,sysN)

impulse(sys1,sys2,...,sysN,t)

.EXAMPLE:

Transfer function.=

53
GRAPH:

54
THEORETICAL CALCULATIONS:

RESULT:

55
 16.RAMP RESPONSE OF A STATE MODEL

AIM:
To obtain ramp response of a state model for a given system.

APPARATUS:
Software: MATLAB

THEORY:
Ramp
A unit ramp is defined in terms of the unit step function, as such:

It is important to note that the ramp function is simply the integral of the unit step function:

Laplace Transfrom of ramp function :

L[ ]= =

MATLAB PROGRAM:
t=0:0.01:10;
u=t
A=input('enter matrix A')
B=input('enter matrix B')
C=input('enter matrix C')
D=input('enter matrix D')
lsim(A,B,C,D,u,t)

PROCEDURE:
lsim simulates the (time) response of continuous or discrete linear systems to arbitrary
inputs.
When invoked without left-hand arguments, lsim plots the response on the screen.

56
 lsim(sys,u,t) produces a plot of the time response of the LTI model sys to the input time
history t,u.
The vector t specifies the time samples for the simulation and consists of regularly spaced
time samples. t = 0:dt:Tfinal
The matrix u must have as many rows as time samples (length(t)) and as many columns
as system inputs. Each row u(i,:) specifies the input value(s) at the time sample t(i) .
The LTI model sys can be continuous or discrete, SISO or MIMO. In discrete time, u
must be sampled at the same rate as the system (t is then redundant and can be omitted or
set to the empty matrix).
In continuous time, the time sampling dt=t1-t2 is used to discretize the continuous model.
If dt is too large (undersampling), lsim issues a warning suggesting that you use a more
appropriate sample time, but will use the specified sample time.
lsim(sys,u,t,x0) further specifies an initial condition x0 for the system states. This syntax
applies only to state-space models.

EXAMPLE:

Transfer function.=

THEORETICAL CALCULATIONS:

57
GRAPH:

RESULT:

58
 17. PID CONTROLLER

AIM:
To control the closed loop system using PID controller.

APPARATUS:
Software: MATLAB

THEORY:
PID controllers are commercially successful and widely used as controllers in industries. For
example, in a typical paper mill there may be about 1500 controllers and out of these 90 percent
would be PID controllers. The PID controller consists of a proportional mode, an Integral mode
and a Derivative mode. The first letters of these modes make up the name PID controller.
Depending upon the application one or more combinations of these modes are used. For
example, in a liquid control system where we want zero steady state error, a PI controller can be
used and in a temperature control system where zero stead state error is not specified, a simple P
controller can be used.
The equation of a PID controller in time-domain is given by

Where is the proportional gain, is the integral reset time and is the derivative time of the
PID controller, m(t) is the output of the controller and e(t) is the error signal given by e(t)=r(t)-
c(t).
The controller used here is a PID controller represented by a block PID and the system or plant is
represented by G(s). R(s) and D(s) are reference signal and disturbance signal respectively. Y(s),
E(s) and M(s) are the output, error and controller output of the system respectively. For the
purpose of good control, we require the system output Y(s) to track any reference signal F(s) and
at the same time reject or suppress deviation due to the disturbance signal D(s). Hence the PID
controller can realize this objective.
Proportional controller:

59
A proportional controller has a proportional term alone. The output of a proportional controller is
proportional to the error e(t) . The equation representing the proportional controller in time
domain is

M(t) =

MATLAB PROGRAM:
num=input('enter the numerator of the transfer function')
den=input('enter the denominator of the transfer function')
h=tf(num,den)
[gm pm wcp wcg]=margin(h)
km=10*(gm/20)
wm=wcp
kp=0.6*km
ki=(kp*wm)/pi
kd=(kp*ki)/(4*wm)
h1=tf([1,0],[1])
g=(kp+(kd*h1)+(ki/h1))*h
bode(g)

PROCEDURE:

Note down the given transfer function.

Sketch the bode plot for the given transfer function by Factor the transfer function into
pole-zero form.
Find the frequency response from the Transfer function.
Use logarithms to separate the frequency response into a sum of decibel terms
Use w=0 to find the starting magnitude.
The locations of every pole and every zero are called break points. At a zero breakpoint,
the Slope of the line increases by 20dB/Decade. At a pole, the slope of the line decreases
by 20dB/Decade.
At a zero breakpoint, the value of the actual graph differs from the value of the staright-
line graph by 3dB. A zero is +3dB over the straight line, and a pole is -3dB below the
straight line.
Sketch the actual bode plot as a smooth-curve that follows the straight lines of the
previous point, and travels through the breakpoints

If A is positive, start your graph (with zero slopes) at 0 degrees. If A is negative, start
your graph with zero slope at 180 degrees (or -180 degrees, they are the same thing).
For every zero, slope the line up at 45 degrees per decade when w= (1 decade before
the Break frequency). Multiple zeros means the slope is steeper
For every pole, slope the line down at 45 degrees per decade when w = (1 decade
before the break frequency). Multiple poles means the slope is steeper.
Find out the gain margin, phase margin, wcp and wcg

60

From those calculate km,wm,kp,ki,kd

Already transfer function h1 is given. Now find out the transfer function g and sketch
bode plot for g by repeating steps from 2 -13.
EXAMPLE:

Given Transfer function =

THEORITICAL CALCULATIONS:

A=

W A
0.01
0.1
1
5
7
10
100

Phase plot:

= -90 - ( - ( - ( - (w

W
0.01
0.1

61
1
5
7
10
100

For the above transfer function

Gain margin =
Phase margin =

Wcp =
Wcg =
Now

Km = 10 x

=
Wm = wcp =
Kp = 0.6 x km =
Ki = (kp x km)/pi

=
Kd = (kp x ki)/(4 x wm)
=
Transfer function h1 = s
g = [kp + (kd x h1) + (ki/h1)] x h
=

Bode plot for transfer function g:

62
Magnitude Plot:
A=

W A
0.01
0.1
1
5
7
10
50
100
200
500
1000

Phase Plot:

W
0.01
0.1
1
5
7
10
50
100
200
500
1000

THEORETICAL CALULATIONS:
63
enter the numerator of the transfer function[ ]
num =

enter the denominator of the transfer function[ ]

den =

Transfer function:
gm =

pm =
wcp =
wcg =
km =
wm =
kp =

ki =
kd =

Transfer function:

64
GRAPH:

65
gm =
pm =

wcp =
wcg =
km =
wm =
kp =
ki =

kd =

Transfer function:

RESULT:

66
 18.LAG COMPENSATOR
AIM: To design a lag compensator for a closed loop system.

APPARATUS:
Software: MATLAB
THEORY:
we can manipulate TF, SS, and ZPK models using the arithmetic and model interconnection
operations described in Operations on LTI Models and analyze them using the model analysis
functions, such as bode and step. FRD models can be manipulated and analyzed in much the
same way you analyze the other model types, but analysis is restricted to frequency-domain
methods.
Using a variety of design techniques, you can design compensators for systems specified with
TF, ZPK, SS, and FRD models. These techniques include root locus analysis, pole placement,
LQG optimal control, and frequency domain loop-shaping. For FRD models, you can either:
Obtain an identified TF, SS, or ZPK model using system identification techniques. Use
frequency-domain analysis techniques.

Other Uses of FRD Models:

FRD models are unique model types available in the Control System Toolbox collection of LTI
model types, in that they don't have a parametric representation. In addition to the standard
operations you may perform on FRD models, you can also use them to: Perform frequency-
domain analysis on systems with nonlinearities using describing functions. Validate identified
models against experimental frequency response data.

MATLAB PROGRAM:
num= input (enter the numerator)
den= input (enter the denominator)
h= tf (num, den)

kv= input (enter kv)

phm= input (enter phase margin)
h1= tf ([1 0], [1])
m= dcgain (h1*h)
67
g= k*h
[mag, phase, w]= bode (g)

e= input (enter margin of safety)

bode (g)
theta= phm+e;
bm= theta-180;
wcm= input (enter wcm corresponding to bm)
a= input (enter gain corresponding to wcm)

beta= 10^(a/20)
w2= wcm/4
tou= 1/w2
w1= 1/(beta*tou)
g1= (h1+w2)/(h1+w1)
g2= g1*g

[gm1 pm1 wcg1 wcp1]= margin(g2)

bode (g2)
hold
bode (g)
if ( (wcg1<wcg)//(wcp1<wcp) )
disp (network is not lag compensator)
else

disp (network is not lag compensator)

end

PROCEDURE:

Write the program in MATLAB editor and save it

Enter the values of numerator and denominator of the uncompensated system given when
asked during execution
Also enter the values of kv, phase margin and margin of safety
bode (g) returns the bode plot of gain adjusted but uncompensated system
68
 bode (g2) returns the bode plot of compensated sytem

EXAMPLE:

Given open loop transfer function G(s)= it is designed to compensate the system so
that static velocity error constant kv= 5 sec-1. The phase margin is at least 40 and gain margin is
10 db
THEORITICAL CACULATIONS:

69
GRAPH:

70
RESULT:

71
 19. LEAD COMPENSATOR
AIM: To design a lead compensator for a closed loop system

APPARATUS:
Software: MATLAB

THEORY:
we can manipulate TF, SS, and ZPK models using the arithmetic and model interconnection
operations described in Operations on LTI Models and analyze them using the model analysis
functions, such as bode and step. FRD models can be manipulated and analyzed in much the
same way you analyze the other model types, but analysis is restricted to frequency-domain
methods.
Using a variety of design techniques, you can design compensators for systems specified with
TF, ZPK, SS, and FRD models. These techniques include root locus analysis, pole placement,
LQG optimal control, and frequency domain loop-shaping. For FRD models, you can either:
Obtain an identified TF, SS, or ZPK model using system identification techniques. Use
frequency-domain analysis techniques.
Other Uses of FRD Models:
FRD models are unique model types available in the Control System Toolbox collection of LTI
model types, in that they don't have a parametric representation. In addition to the standard
operations you may perform on FRD models, you can also use them to: Perform frequency-
domain analysis on systems with nonlinearities using describing functions. Validate identified
models against experimental frequency response data.

MATLAB PROGRAM:
z

bm=theta-180;

wcm=input('enter wcm corresponding to bm')

w1=wcm*sqrt(alpha)

w2=wcm/sqrt(alpha)

g1=tf([1/w1 1],[1/w2 1])

72
g2=g1*g
[mag3, phase3, w3]=bode(g2)

bode(g2)
hold
bode(g)

PROCEDURE:

Write the program in MATLAB editor and save it

Enter the values of numerator and denominator of the uncompensated system given when
asked during execution
Also enter the values of kv, phase margin and margin of safety
bode (g) returns the bode plot of gain adjusted but uncompensated system
bode (g2) returns the bode plot of compensated sytem

EXAMPLE:

is designed to compensate for the system so that the static velocity error constant
is 20 sec-1 and phase margin is 50 and gain margin is atleast 10 db

THEORITICAL CALCULATIONS:

Given that

We have

The magnitude and phase plots for open loop transfer function for uncompensated system can be
given as

Mag (db)

Any lead compensator will be of the form

73
The compensated system will have the open loop transfer function as

Given that kv= 20 sec-1

Therefore

Value of

This gain is achieved at a critical frequency

Therefore the critical frequencies for the lead compensator are

Whose values are equal to 4.41 rad/sec and 18.4 rad/sec

Thus the lead compensator is

Where

Therefore the transfer function of compensated system is given by

Therefore the open loop transfer function of the compensated system is

74
GRAPH:

75
RESULT:

76
 20. LAG- LEAD COMPENSATOR

AIM:
To design lag-lead compensator using closed loop system.

APPARATUS:
Software: MATLAB

THEORY:

A leadlag compensator is a component in a control system that improves an

undesirable frequency response in a feedback and control system. It is a fundamental building
block in classical control theory.

lagts delays a financial time series object by a specified time step.

newfts = lagts(oldfts) delays the data series in oldfts by one time series date entry and returns the
result in the object newfts. The end will be padded with zeros, by default.
newfts = lagts(oldfts, lagperiod) shifts time series values to the right on an increasing time scale.
lagts delays the data series to happen at a later time. lagperiod is the number of lag periods
expressed in the frequency of the time series object oldfts. For example, if oldfts is a daily time
series, lagperiod is specified in days. lagts pads the data with zeros (default).
newfts = lagts(oldfts, lagperiod, padmode) lets you pad the data with an arbitrary value, NaN, or
Inf rather than zeros by setting padmode to the desired value.
leadts advances a financial time series object by a specified time step.

newfts = leadts(oldfts) advances the data series in oldfts by one time series date entry and returns
the result in the object newfts. The end will be padded with zeros, by default.

newfts = leadts(oldfts, leadperiod) shifts time series values to the left on an increasing time scale.
leadts advances the data series to happen at an earlier time. leadperiod is the number of lead
periods expressed in the frequency of the time series object oldfts. For example, if oldfts is a
daily time series, leadperiod is specified in days. leadts pads the data with zeros (default).
newfts = leadts(oldfts, leadperiod, padmode) lets you pad the data with an arbitrary value, NaN,
or Inf rather than zeros by setting padmode to the desired value.

77
MATLAB PROGRAAM:
num=input('enter the numerator')
den=input('enter the denominator')
h=tf(num,den)
kv=input('enter velocity error')
phm=input('enter the phase margin')
h1=tf([1 0],[1])
m=dcgain(h1*h)
k=kv/m
g=k*h
[mag phase w]=bode(g)
[gm pm wcg wcp]=margin(g)
e=input('enter margin of safety')
bode(g)
theta=phm+e;
bm=theta-180;
wcm=('enter wcm corresponding to bm')
a=input('enter gain corresponding to wcm')
beta=10^(a/20)
w21g=wcm/4
tou=1/w21g
w11g=1/(beta*tou)
g1=(h1+w21g)/(h1+w11g)
theta1d=theta1d*(pi/180);
alpha=(1-sin(theta1d)/(1+sin(theta1d)))
a=-20*log10(1/sqrt(alpha))
wcm1d=input('enter the value of wcmld corresponding to gain a1')
w11d=wcm1d*sqrt(alpha)
w21d=wcm1d/sqrt(alpha)
g3=tf([1/w11d1],[1/w21d 1])
g4=g3*g1*g
[mag3 phase3 w3]=bode(g2)
bode(g)
hold
bode(g4)

PROCEDURE:
Numerator of the given transfer function is assigned to num
Denominator of the transfer function is assigned to den
The value of the static velocity constant is assigned to the kv
78
 Margin of the safty is assigned to e
Plot is obtained by in-build function bode()
Wcm values assigned to wcm which is obtained from above bode plot
The gain corresponding to wcm is assigned to a
If wcg1>wcg orr wcp1>wcp, the network is compensated otherwise it is not
compensated.

THEORETICAL CALCULATIONS:

Transfer function=

For complex w,
Gain =

MAGNITUDE PLOT:

Factor Corner frequency Log magnitude in db

=-20log(factor)

PHASE PLOT:
=

W(rad/sec)

79
New phase margin required is =
Frequency corresponding to

RESULT:

80
 21.NYQUIST PLOT FROM TRANSFER FUNCTION
AIM:
To obtain the Nyquist plot for a given transfer function of the system.

APPARATUS:
Software: MATLAB

THEORY:
A nyquist plot is used in automatic control and signal processing for assessing the stability of a
system with feedback. It is represented by a graph in polar coordinates in which the gain and
phase of a frequency response are plotted. The plot of these phasor quantities shows the phase as
the angle and the magnitude as the distance from the origin. This plot combines the two types of
Bode plot magnitude and phase on a single graph with frequencry as a parameter along the
curve.

Nyquist calculates the Nyquist frequency response of LTI models. When invoked without left-
hand arguments, nyquist produces a Nyquist plot on the screen. Nyquist plots are used to analyze
system properties including gain margin, phase margin, and stability.
The nyquist stability criterion , provides a simple test for stability of a closed-loop control system
by examining the open-loop system's Nyquist plot. Stability of the closed-loop control system
may be determined directly by computing the poles of the closed-loop transfer function. The
Nyquist Criteria can tell us things about the frequency characteristics of the system. For
instance, some systems with constant gain might be stable for low-frequency inputs, but become
unstable for high-frequency inputs. Also, the Nyquist Criteria can tell us things about the phase
of the input signals, the time-shift of the system, and other important information.
The Nyquist Contour
The nyquist contour, the contour that makes the entire nyquist criterion work, must encircle the
entire right half of the complex s plane. Remember that if a pole to the closed-loop transfer
function (or equivalently a zero of the characteristic equation) lies in the right-half of the s plane,
the system is an unstable system.To satisfy this requirement, the nyquist contour takes the shape
of an infinite semi-circle that encircles the entire right-half of the s plane.
Nyquist Criteria
Let us first introduce the most important equation when dealing with the Nyquist criterion:

Where:
N is the number of encirclements of the (-1, 0) point.
Z is the number of zeros of the characteristic equation.
81
 P is the number of poles of the characteristic equation.
With this equation stated, we can now state the Nyquist Stability Criterion:
Nyquist Stability Criterion
A feedback control system is stable, if and only if the contour in the F(s) plane does not
encircle the (-1, 0) point when P is 0.
A feedback control system is stable, if and only if the contour in the F(s) plane encircles
the (-1, 0) point a number of times equal to the number of poles of F(s) enclosed by .
In other words, if P is zero then N must equal zero. Otherwise, N must equal P. Essentially, we
are saying that Z must always equal zero, because Z is the number of zeros of the characteristic
equation (and therefore the number of poles of the closed-loop transfer function) that are in the
right-half of the s plane.

MATLAB PROGRAM:
num=input(enter the numerator of the transfer function)

den=input(enter the denominator of the transfer function)

h=tf(num,den)

nyquist(h)

[gm pm wcp wcg]=margin(h)

if(wcp>wcg)

disp(system is stable)

else

disp(system is unstable)

end

PROCEDURE:

Write MATLAB program in the MATLAB editor document.

Then save and run the program.
Give the required input.
The syntax tf(num,den) solves the given transfer function and gives poles and zeros of
the function.
nyquist(sys), nyquist calculates the Nyquist frequency response of LTI models. When
invoked without left-hand arguments, nyquist produces a Nyquist plot on the screen.
Nyquist plots are used to analyze system properties including gain margin, phase margin,
82
 and stability. nyquist(sys) plots the Nyquist response of an arbitrary LTI model sys.
This model can be continuous or discrete, and SISO or MIMO. In the MIMO case,
nyquist produces an array of Nyquist plots, each plot showing the response of one
particular I/O channel. The frequency points are chosen automatically based on the
system poles and zeros.
[Gm,Pm,Wcg,Wcp] = margin(sys), margin calculates the minimum gain margin, phase
margin, and associated crossover frequencies of SISO open-loop models. The gain and
phase margins indicate the relative stability of the control system when the loop is closed.

RESULT:

83
 22.SYSTEM IDENTIFICATION OF DC MOTOR USING MATLAB AND
LABVIEW
AIM:

To find out the transfer function using system identification toolbox

APPARATUS:
Software: MATLAB and LABVEIW
THEORY:

The transfer function of the dc motor from the dc motor-generator set can directly be
determined with the help of Matlab and Labview. Since the transfer function is the ratio of
output to input Laplace transforms with zero initial conditions, here the input and output is taken
as motor voltage and speed respectively. A VI (virtual instrument) is developed with Labview
that allows the DAQ to read a user selected reference voltage continuously. The variation of
input armature voltages and the corresponding output values (speed) are tabulated in the excel
file. The excel file is linked with System identification toolbox in MATLAB which is used for
the determination of parameters of the dc shunt motor. (zeta),Tw,Td,k are the constants that are
to be determined in the experiment.

System identification is a methodology for building mathematical models of dynamic

systems using measurements of the systems input and output signals. System Identification
Toolbox constructs mathematical models of dynamic systems from measured input-output data.
You can use time-domain and frequency-domain input-output data to identify continuous-time
and discrete-time transfer functions, process models, and state-space models. You can use the
identified model for prediction of system response and for simulation in Simulink. The toolbox
also lets you model time-series data and perform time-series forecasting.

Transfer Function of a dc motor:

Using Laplace Transforms, the above modeling equations can be expressed in terms of s.

S (Js+b) (s) =KI(s) (1)

By eliminating I(s) we can get the following open-loop transfer function, where the rotational
speed is the output and the voltage is the input.

(2)

84
 Figure1: Step response of an open loop system

The step response of a system (Fig.3) in a given initial state consists of the time evolution of its
outputs when its control inputs are Heaviside step functions. In control theory, step response is
the time behavior of the outputs of a general system when its inputs change from zero to one in a
very short time.

From a practical standpoint, knowing how the system responds to a sudden input is important
because large and possibly fast deviations from the long term steady state may have extreme
effects on the component itself and on other portions of the overall system dependent on this
component.

PROCEDURE:

Step 1:

Construct the simulation diagram for closed loop control of dc motor using Labview as
shown in fig2.The armature voltage supplied to the motor is controlled by lab view software
using DAQ interface and the DAQ assistant is used in signal generation mode for this operation.
Thus, by making use of virtual knob in lab view, the input to the dc motor is varied such that as
the position of the knob indicator varies, the input armature voltage also varies.

As the voltage varies, the speed of the motor varies accordingly and can be observed
using the signal analyzer. The voltage is to be taken to maximum first and then again is brought
to minimum position.

STEP 2: By making use of merger, the values of voltage vs. speed are utilized for creating an
excel sheet. After the creation of Excel file, it is imported into matlab and the data object is
created.

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 FIG 2:Closed loop contro of dc motor using LABVIEW

STEP 3:

After importing the file into MATLAB command window, to start up the System Id toolbox, use
the command ident. A GUI (graphical user Interface) for the toolbox will appear.

STEP 4:
In the System Identification Tool, select Import data > Time domain data. This action
opens the Import Data dialog box. In the Import Data dialog box, select Data Format for
signals > Data Object specify the following options:

ObjectEnter the object name

Starting time Enter 0 as the starting time. This value designates
the starting value of the time axis on time plots.
Sampling interval Enter 1 as the time between successive samples in seconds. This value is
the actual sampling interval in the experiment.

STEP 5:
Click Import to add the data to the System Identification Tool. The tool displays an icon
to represent the data..

Step 6: Select remove trends from the preprocess option in window

Step 7: After selecting the remove trends operation, the range of the samples for estimation and
validation are to be selected (Half the samples under estimation and remaining half under
validation).

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Step 8: After estimating and validating the samples, select the transfer function model from the
estimate option, Select the poles as 2 (generally systems are second order and underdamped) and
then select estimate.

Step 9: After that the window appears and then double-click on the tf block. Now the data file
is linked to MATLAB, where it is assigned to a variable. The input and the output i.e., voltage
and speed respectively are assigned to variables for the purpose of processing.

Then performing the above mention procedures, the transfer function is obtained .
Experimental Data:

S.No Voltage(V) Speed (rpm)

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RESULT:

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 23.REALIAZATION OF TRANSFER FUNCTION USING OP-AMPS
AIM:

To find realization of transfer function using Op-Amps.

APPARATUS:

Software: MATLAB

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