18-07-2012, 04:53 PM
Speed Control of DC Motor by Fuzzy Controller
Speed Control of DC Motor.pdf (Size: 339 KB / Downloads: 158)
Abstract
This paper presents speed
control of a separately excited DC
motor using fuzzy logic control (FLC)
based on Matlab Simulation program.
This method of speed control of a dc
motor represents an ideal application
for introducing the concepts of fuzzy
logic. The paper shows how a
commercially available fuzzy logic
development kit can be applied to the
theoretical development of a fuzzy
controller for motor speed, which
represents a very practical class of
engineering problems.
INTRODUCTION
Classic Control has proven for a long
time to be good enough to handle
control tasks on system control,
however his implementation relies on
an exact mathematical model of the
plan to be controller and not simple
mathematical operations. The fuzzy
logic, unlike conventional logic system,
is able to model inaccurate or imprecise
models.
SYSTEM DESCRIPTION
Motor Model
The resistance of the field winding and
its inductance of the motor used in this
study are represented by Rf and La
respectively in dynamic model.
Armature reactions effects are ignored
in the description of the motor. This
negligence is justifiable to minimize the
effects of armature reaction since the
motor used has either interpoles or
compensating winding. The fixed
voltage Vf is applied to the field and
the field current settles down to a
constant value. A linear model of a
simple DC motor consists of a
mechanical equation and electrical
equation as determined in the following
equations.
Defining membership functions and
rules :
System speed comes to reference value
by means of the defined rules. For
example, first rule on Table
1determines, 'if (e) is NL and (ce) is NL
than (ca) is PL'. According to this rule,
if error value is negative large and
change of error value is negative large
than output, change of alpha will be
positive large. To be calculated FLC
output value, the inputs and outputs
must be converted from 'crisp' value
into linguistic form. Fuzzy membership
functions are used to perform this
conversion. In this paper, all
membership functions are defined
between -1 and 1 interval by means of
input scaling factors K1E and K2CE,
and output t scaling factor K3c. Thus,
since simple numbers are now
processed in controller after scaling,
fuzzy computation is performed.
CONCLUSIONS
The results of experiment on the real
plant demonstrate that the proposed
fuzzy logic controller is able to
sensitiveness to variation of the
reference speed attention.
Speed Control of DC Motor.pdf (Size: 339 KB / Downloads: 158)
Abstract
This paper presents speed
control of a separately excited DC
motor using fuzzy logic control (FLC)
based on Matlab Simulation program.
This method of speed control of a dc
motor represents an ideal application
for introducing the concepts of fuzzy
logic. The paper shows how a
commercially available fuzzy logic
development kit can be applied to the
theoretical development of a fuzzy
controller for motor speed, which
represents a very practical class of
engineering problems.
INTRODUCTION
Classic Control has proven for a long
time to be good enough to handle
control tasks on system control,
however his implementation relies on
an exact mathematical model of the
plan to be controller and not simple
mathematical operations. The fuzzy
logic, unlike conventional logic system,
is able to model inaccurate or imprecise
models.
SYSTEM DESCRIPTION
Motor Model
The resistance of the field winding and
its inductance of the motor used in this
study are represented by Rf and La
respectively in dynamic model.
Armature reactions effects are ignored
in the description of the motor. This
negligence is justifiable to minimize the
effects of armature reaction since the
motor used has either interpoles or
compensating winding. The fixed
voltage Vf is applied to the field and
the field current settles down to a
constant value. A linear model of a
simple DC motor consists of a
mechanical equation and electrical
equation as determined in the following
equations.
Defining membership functions and
rules :
System speed comes to reference value
by means of the defined rules. For
example, first rule on Table
1determines, 'if (e) is NL and (ce) is NL
than (ca) is PL'. According to this rule,
if error value is negative large and
change of error value is negative large
than output, change of alpha will be
positive large. To be calculated FLC
output value, the inputs and outputs
must be converted from 'crisp' value
into linguistic form. Fuzzy membership
functions are used to perform this
conversion. In this paper, all
membership functions are defined
between -1 and 1 interval by means of
input scaling factors K1E and K2CE,
and output t scaling factor K3c. Thus,
since simple numbers are now
processed in controller after scaling,
fuzzy computation is performed.
CONCLUSIONS
The results of experiment on the real
plant demonstrate that the proposed
fuzzy logic controller is able to
sensitiveness to variation of the
reference speed attention.