INTRODUCTION CONCLUSION ICTS2005 The Proceeding

Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 159 MOTOR DC POSITION CONTROL BASED ON MOVING SPEED CONTROLLED BY SET POINT CHANGING USING FUZZY LOGICS CONTROL SYSTEM Andino Maseleno 1 , Fajar Hayyin 2 , Hendra 3 , Rahmawati Lestari 4 , Slamet Fardyanto 5 , Yuddy Krisna Sudirman 6 1 Informatics Engineering, National Development University “Veteran”, Yogyakarta Jl. Babarsari No.2 Yogyakarta 55281, Indonesia 4 Electronics Engineering, Indonesian Islamic University, Yogyakarta Jl. Kaliurang Km.14,5 Yogyakarta, Indonesia 6 , 5 , 3 , 2 Industrial Engineering, National Development University “Veteran”, Yogyakarta Jl. Babarsari No.2 Yogyakarta 55281, Indonesia andinomaselenoyahoo.com, rahma_124yahoo.com, peacefiestayahoo.com, hauna81yahoo.com, f_ardyantoyahoo.com, krisna_dewayahoo.com ABSTRACT One of motor DC function is uses for position control. It is extremely need high accuracy to reach the position. Therefore, a control system that able to arrange motor DC position as we like is needed. There is one familiar control system in nowadays which is implemented on microcontroller using fuzzy logics control system. These fuzzy logics system had 2 crisp input which is; error position e_pos and position change d_pos and had a 1 crisp output is voltage changing. Each function membership has 5 labels then uses 25 basis rules. Defuzzification using mean of maxima methods. Defuzzification output resulted by microcontroller as digital bits that converted by DAC Digital Analog Converter into analog as voltage. This voltage will operate the motor DC. The Feedback system came from output of position sensor as pulses. Respond system tested with various set points, the result already shown a quite good respond. Fuzzy logics control had a high level of accuracy and softer in dealing with respond, changing set point value for instance. Keywords : Motor DC, Fuzzy logics, Microcontroller

1. INTRODUCTION

Recent days, motor DC as driver system still play an important role because it controllable. In this case we try to change motor DC into position driver. This system need a highly accuracy in order to reach the correct position. A good motor positioning control system is needed so motor able to move to whatever direction we want. There is so many control system. Fuzzy logics control gives an alternative control system. Fuzzy logics system done by extract rules so appropriate with mind though and human knowledge both; operator or expert, so we does need math model. Fuzzy logics recently successfully made a brake through to all the problems ever met and soon become a high technology basic. In application, it belief that capable to create a revolution in technology. Implemented on control system is a innovation brake through in control system. Because control system using fuzzy more precise than digital control system that only control on off on some device. This research focus in effort to build a motor DC position control system using microcontroller AT89C52. Microcontroller able to cover industrial needs and recently many used, compiler assembler and supporting device easy to get.

2. FUZZY LOGIC

Basically, fuzzy logics used to reveal all the logics structure limitations that only had two points of view which is true or false. Fuzzy logics try to make a solution to links those condition that unable to dissolve only using statement yes or no but also give other description or between conditions yes or no in math.

2.1. Fuzzy Set

Universes of discourse define fuzzy set into basic theory fuzzy set. For a universes of discourses U, Fuzzy compilation based on memberships function located U universes of discourse membership into some degree that had value among 0 and 1. Membership function is a curve that shows math on data input points to membership value that had interval between 0 and 1. Membership function usually used in real live is: a. S Membership function µ[x] = [ ] [ ] ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ ≤ ≤ − − − ≤ ≤ − − c u c u b a c c u b u a a c a u a u 1 2 1 2 2 2 Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 160 Defuzificatio Control Rule Fuzzification µ 1 0,5 0 a b c Figure 1 S Membership function b. Linear membership function Input mapping to membership degree figures as straight line. x ≤ a µ[x] = x-a b-a a ≤ x ≤ b 1 x ≥ b µ 1 0 a b Figure 2 Linear membership function c. Triangle Membership Function Triangle curve basically is a merge between 2 linear lines. µ[x] = µ 1 0 a b c Figure 3 triangle membership functions d. Trapezium Membership function Basically, Trapezium membership similar as triangle form, only some other spot that had 1 memberships. µ[x] = µ 1 0 a b c d Figure 4 Trapezium membership function

2.2. Fuzzy Logics Control

In general, Fuzzy logics control is close loop system. Basic fuzzy logics control contained fuzzification unit, decision maker mechanisms and inference system, and defuzzification unit, shown at the next figure. Crisp Output Fuzzy Output Crisp Input Fuzzy Input Figure 5 basic structure of fuzzy logics control

3. SYSTEM PLANNING

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.The Concept ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ ≤ ≤ − − ≤ ≤ − − c x c x b b c x c b x a a b a x a u ⎪ ⎪ ⎪ ⎩ ⎪⎪ ⎪ ⎨ ⎧ ≥ ≤ ≤ − − ≤ ≤ ≤ ≤ − − ≤ d x d x c c d x d c x b b x a a b a x a x 1 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 161 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. Figure 6 Diagram block Motor DC position Control system Figure 7 Error Position Membership function e_pos Figure 8 Position change membership function System µC AT89C52 FLC Position Sensor MOTOR DC SET POINT DAC DISPLAY Amplifier Position Counter and d_pos pos e_pos pos NB NS Z PS PB 50 100 150 200 250 300 - i70 - 85 170 255 Error position 85 - 255 Note: NB = negative big membership set NS = negative small membership set Z = zero membership set PS = positive small membership 5 10 15 20 25 30 - 255 - 32 - 65 -32 - 65 - 255 Position change d_pos Note: NB = negative big membership set NS = negative small membership set Z = zero membership set PS = positive small membership 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

Control system using Fuzzy Logic planned to gather the correct motor DC position control system for variety set point score the position we want to. Control system had been tested for variety set point score in this research. YES N YES YES YES YES Start Initialization Check FLC ? FLC Activate sampling timer Save data in RAM display Start button pushed? Turn off sampling timer Stop button pushed ? Keypad pushed ? Number button pushed? N N N N Figure 11 Main Program Flowchart 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 165

4.1. Digital To Analog Converter DAC

The planning system is using 1one DAC 0832 function as input voltage control for motor driver. Next is DAC testing table with variety binary input. Table 2 DAC testing table No. Input Binary Output V Measured 1. 0000 0000 2. 0000 0010 - 2.42 3. 0001 0000 - 2.23 4. 1000 0000 0.65 5. 1111 1111 2.6

4.2. Motor Driver

Motor driver circuit input is Digital to Analog Converter DAC output. Motor driver circuit process analog signal in voltage from DAC to control appropriate motor spinning. For direction control application using microcontroller, is using transistor as in figure 12, contained 4 transistors with Darlington configuration in each transistor contained 2 transistors which is Tip 31A and C9013 for NPN, and Tip 32A and C9012 for PNP.

4.3. Position Sensor

There are 2 sensors to distinguish left or right spinning direction. Sensor circuit contained optocoupler, encoder, lm358 as comparator, and 74HC14 schmit trigger. Optocoupler Optocoupler is an optic sensor, which is capable to change light effect into electric pulse. Inside the optocoupler contained infrared LED and phototransistor. If optocoupler activated, it able to transfer electric energy from infrared LED to phototransistor. For further if infrared light is block then basis collector obstacle getting bigger so collector current decrease and collector voltage closing 0 volt, electric transfer will cut off. Figure 12 Two direction position sensor output Encoder Plastic plate giving black and white stripes on plate fingers with same distance in 180 lines. Each line will read by optocoupler in pulses. To distinguish direction then the line made up and down with different pattern. 74HC14 Schmitt Trigger IC 74HC14 function as square wave inverter amplifier. The imperfect square wave related to Schmitt trigger 74HC14 input, in order to gather the perfect square wave.

4.4. Display Unit

To display set point and motor position using 6 seven segment common cathode. IC input Port came from port B PPI 8255 used as data sender. Display unit using IC CMOS 4511 with BCD input and as decoder that separate set point display and position used by 74HC138. The output data from microcontroller in 8 bit binary will be split into 2 parts. First part contains 4 bit A – D used as IC CMOS 4511 input to display at seven segment. The next 4 bit D0 – D3 used as IC 74HC138 input to address which seven segment will turn on.

4.5. Testing Result Table

This test is to watch the system respond against given set point score. Testing doing by giving variety set point. Table 3 Testing Result in variety set point No. Set point Position Error 1. 100 100 - 2. 450 448 2 3. 250 250 - 4. 180 184 4 5. 360 360 - 6. 300 302 2 7. 20 22 2 8. 128 128 - 9. 99 100 1 10. 75 74 1

5. CONCLUSION

Based on testing result and system analyze, conclusion of all these is: 1. Fuzzy logic control relative easier to be implemented because doesn’t need math model but operate based on rule that extractable from experience and operator skill. 2. Fuzzy logic control system very sensitive against membership function variables, fuzzy rule and input variable type, because those variables influence parameter size from position and position change. Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 166 3. More fuzzy set and membership from each fuzzy set will get higher accuracy level and softener in dealing with respond. 4. Fuzzy logic control has higher accuracy level and softener in dealing with respond, for example; with set point score change. Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 167 A VARIABLE-CENTERED INTELLIGENT RULE SYSTEM Irfan Subakti Department of Informatics, Faculty of Information Technology Institute Technology of Sepuluh Nopember Surabaya ITS Kampus ITS, Keputih, Sukolilo, Surabaya, Indonesia email: yifanagmail.com ABSTRACT A Rule-based System RBS is a good system to get the answer of What, How, and Why questions from the rule base RB during inferencing. Answers and explanations are properly provided. The problem with RBS is that it can’t easily perform the knowledge acquisition process and it can’t update the rules automatically. Only the expert can update them, manually, by the support of a knowledge engineer. Moreover most researches in RBS concern more about the optimization of the existing rules than about generating new rules from them. Rule optimization, however, can’t change the result of the inferencing, significantly, in term of knowledge coverage. Ripple Down Rules RDR came up to overcome the major problem of expert systems: experts no longer always communicate knowledge in a specific context. RDR allows for extremely rapid and simple knowledge acquisition without the help of a knowledge engineer. The user doesn’t ever need to examine the RB in order to define new rules: the user only needs to define a new rule that correctly classifies a given example, and the system can determine where the rule should be placed in the hierarchy. The limitation of RDR is the lack of powerful inference. RDR seems to use Depth First Search which lacks the flexibility of question answering and explanation accrued from inference.A Variable-Centered Intelligent Rule System VCIRS is our proposed method. It hybridizes RBS and RDR. The system architecture is adapted from RBS and obtains advantages from RDR. This system organizes the RB in a special structure so that easy knowledge building, powerful knowledge inferencing and evolutional improvement of system performance can be obtained at the same time. The term “Intelligent” stresses that it can “learn” to improve the system performance from the user during knowledge building via value analysis and refining by rule generation. Keywords : Rule-based Systems, Ripple Down Rules, knowledge building, knowledge inferencing, knowledge refining

1. INTRODUCTION