Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 162
µ
F
output 255 NB NS
Z PS
PB
0 64 128 192 255
Output
Figure 9 Output membership function
Figure 10 Fuzzification Flowchart
Set point SP set through the keypad. Set point
will send into minimum system, set point will decreased by present value PV that powered by
position sensor resulting error position e_pos in the minimum system. Although position changing d_pos
are recent error score et minus by previous error score e t-1. These two input gained by position
sensor output. The number of pulse that enters from sensor divided to show recent motor movement. Error
position and position change are fuzzification inputs.
Fuzzy Logic Control FLC System contained fuzzification parts, inference, and defuzification. As
the system revenue is a defuzzification process that consider by 8 bit binary as DAC input. DAC output
will amplify in motor amplifier and the result is voltage to activate the motor. Each PV change will
decrease with SP and the result is error position that will be fuzzification input with position change in that
time again, that so over until the same error with zero or approximately zero.
3.1. Fuzzy Logic Control FLC System Planning
FLC inputs
FLC inputs came from sensor position in pulse. From the sensor then formed two inputs which are
error position e_pos and position change d_pos. Error position e_pos
Error position is a difference between set point position SP and actual position PV.
Error position e_pos = Set point SP – Position PV
3.1
Set point revenue by keypad that had a score between 0
o
– 360
o
with 8 bit resolution powered by microcontroller AT89C52 is 0 until 255.
Position Change d_pos Position change is a difference between recent
error value and previous error value.
d_pos = e t – e t-1 3.2
Start e_pos = SP –
PV d_pos = et –
1 Look at position error and position change
b hi
bl Finish
Motor DC Position Control Based on Moving Speed Controlled by Set Point Changing Using Fuzzy Logics Control System – Andino Maseleno, Fajar Hayyin, Hendra,
Rachmawati Lestari, Slamet Fardyanto, Yuddy Krisna Sudirman
ISSN 1858-1633 2005 ICTS 163
d_pos shows position change with an interval as big as sampling value that had been done.
Determine Membership Function
Membership function derived by using trial and error method. Fuzzy set membership stated in function
definition, by analyze to determine membership degree for each element in universes of discourse.
Universes of discourse value amount in 0 until 255 that shows the lowest and highest value in
microcontroller count. It also had other limitation which incapable to operate in negative sense and will
be acquainted into positive. Error position membership
Fuzzy membership function had different form depends on planning demand. Fuzzy membership
function for error position input is five memberships set in triangle form as follows.
Maximum membership function degree is not made to be same value with 1 but same value with
255, to use microcontroller resolution, so this membership degree became abnormally.
Maximum membership function degree is not made to be same value with 1 but same value with
255, to use microcontroller resolution, so this membership degree became abnormally.
Position change membership d_pos
Using five memberships set in triangle form shows in Figure 8.
Output membership Output membership function is five fuzzy
singleton set had the same label with error and d_pos. Fuzzification
Mapping from domain analog to domain fuzzy set. Fuzzy Set input is position error e_pos and position
change d_pos, fuzzification flowchart can be shown on figure 10.
After result fuzzy score from measurement. Thus, the next step is mapping data into appropriate fuzzy
set with the result that gathering certain membership degree for a membership set. The process doing by
function form for each fuzzy set or using table, it used Table 1 Fuzzy logic control rule
Condition Rule no -
Position Change
d_pos Output
1 Positive Big
Negative Big Negative Big
2 Positive Big
Negative Small Negative Big
3 Positive Big
Zero Negative Big
4 Positive Big
Positive Small Negative Small
5 Positive Big
Positive Big Zero
6 Positive Small
Negative Big Negative Big
7 Positive Small
Negative Small Negative Big
8 Positive Small
Zero Negative Small
9 Positive Small
Positive Small Zero
10 Positive Small
Positive Big Positive Small
11 Zero Negative
Big Negative Big
12 Zero Negative
Small Negative Small
13 Zero Zero
Zero 14
Zero Positive Small
Positive Small 15
Zero Positive Big
Positive Big 16
Negative Small Negative Big
Negative Small 17
Negative Small Negative Small
Zero 18
Negative Small Zero
Positive Small 19
Negative Small Positive Small
Positive Big 20
Negative Small Positive Big
Positive Big 21
Negative Big Negative Big
Zero 22
Negative Big Negative Small
Positive Small 23
Negative Big Zero
Positive Big 24
Negative Big Positive Small
Positive Big 25
Negative Big Positive Big
Positive Big
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 164
to mapping error position membership degree e_pos and position change membership degree d_pos.
The table system choose because easier, simple and quick these and because there is no arithmetic
process. The system lack ness is using a lot of memory, but it does matter because there is enough
memory. Inference Rule Base
The Used Fuzzy Logic rules depend on the control system. There is no exact formulation in determine
fuzzy rules and input output membership function. Fuzzy rule base in these control based on “if –
then” rule, able to shows rule and relation between position error, position change, and output. The
planning is using MAX-MIN method. Defuzzification
Defuzzification process is final part of fuzzy logic set for purpose in mapping fuzzy set from inference
result into the real value. Mean of Maximum MOM used because operationally usable. Defuzzification
solution observed by taking average domain score which has maximum membership score.
Defuzzification membership set is fuzzy singleton set. It use to simplify the system and easier the count.
Recent defuzzification output used to control the voltage to arrange motor position. If output is positive
then the voltage will increase and if output is negative then voltage will decrease.
Software Planning
Fuzzy logic control process doing by the program made using assembler language for microcontroller
AT89C52. These Program reading actual data from position sensor which presentate motor position then
compare with the set point and processing contain fuzzification, inference and defuzzification. Fuzzy
result continued to DAC to control motor position. System responds shows in graphic against time.
4. RESULT AND DISCUSSION
