scholarly journals Kendali Logika Fuzzy pada Sistem Electronic Control Unit (ECU) Air Conditioner Mobil

2019 ◽  
Vol 6 (1) ◽  
pp. 25
Author(s):  
Fahmizal Fahmizal ◽  
Tommy Richard Orlando ◽  
Budi Bayu Murti ◽  
Maun Budiyanto ◽  
Afrizal Mayub
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Electronic Control Unit ◽  
Control Unit ◽  
Electronic Control ◽  
Air Conditioner ◽  
Test Results ◽  
Rotating Speed ◽  
Logic Control ◽  
Fuzzy Input

<p>Makalah ini memaparkan perancangan kendali logika <em>fuzzy</em> pada sistem <em>electronic control unit </em>(ECU)<em> </em><em>air condition</em><em>er </em>mobil. Salah satu kendala pada ECU mobil adalah kerusakan pada sistem <em>air conditio</em><em>ner</em>. Bila ini terjadi umumnya pengguna mobil mengganti sistem ECU secara keseluruhan. Namun pada makalah ini, penulis meracang sistem ECU yang secara khusus digunakan untuk sistem <em>air conditioner</em> mobil. Sistem yang dirancang menggunakan sensor DS18B20 <em>waterproof</em> untuk mendeteksi suhu <em>evaporator</em> mobil. Selanjutnya, nilai suhu tersebut digunakan sebagai masukan logika <em>fuzzy</em> yang terdiri dari pembacaan suhu sekarang dan suhu terakhir dalam mengatur kecepatan putar kipas motor DC atau <em>fan exhausting</em> dan waktu <em>switching</em> <em>magnetic</em> <em>clutch compressor</em> menyala. Hubungan relasi masukan <em>fuzzy</em> diselesaikan dengan aturan Mamdani dan keluaran dari logika <em>fuzzy</em> diselesaikan dengan metode <em>weight average</em> (WA). Dari hasil pengujian diperoleh bahwa kendali logika <em>fuzzy</em> yang diaplikasikan pada rancangan sistem <em>air conditio</em><em>ner</em> mampu mengendalikan kecepatan <em>fan exhausting</em> secara halus dan responsif.</p><p><em><strong>Abstract</strong></em></p><p><em>This paper describes the design of fuzzy logic controls in the system of electronic control unit (ECU) of car air conditioner. One obstacle in the car ECU is damage to the air conditioner system. If this happens generally car users change the whole ECU system. But in this paper, the authors form the ECU system which is specifically used for car air conditioner systems. The system is designed using a DS18B20 waterproof sensor to detect the temperature of the car's evaporator. Furthermore, the temperature value is used as a fuzzy logic input consisting of reading the current temperature and the last temperature in adjusting the rotating speed of the DC motor fan or exhausting fan and when the switching magnetic clutch compressor is on. Completion of fuzzy input relations is solved using Mamdani rules and the output of fuzzy logic is solved using the weight average (WA) method. From the test results it was found that the fuzzy logic control applied to the design of the air conditioner system was able to control the speed of the exhausting fan in a smooth and responsive manner.</em></p><p><strong><br /></strong></p><p> </p>

scholarly journals Design and Implementation Buck Converter for 540WP Solar Charger Using Fuzzy Logic Control

2018 ◽  
Vol 43 ◽  
pp. 01009
Author(s):  
Sutedjo ◽  
Ony Asrarul Qudsi ◽  
Andi Ardianto ◽  
Diah Septi Yanaratri ◽  
Suhariningsih ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Duty Cycle ◽  
Fuzzy Logic Control ◽  
Buck Converter ◽  
Test Results ◽  
Constant Voltage ◽  
Battery Charging ◽  
Set Point ◽  
Design And Implementation ◽  
Logic Control

This paper presents the details of design and implementation of DC-DC Buck converter as solar charger. This converter is designed for charging a battery with a capacity of 100 Ah (Ampere Hours) which has a charging voltage of 27.4 volts. The constant voltage method is selected on battery charging with the specified set point. To ensure the charging voltage is always on the set point, the duty cycle control of buck converter is set using Fuzzy Logic Control (FLC). The design implementation has been tested on PV (photovoltaic) with 540WP capacity. Based on the test results, this method is quite well implemented on the problem charger

scholarly journals IoT Technology with Fuzzy Logic Control Integrated System to Achieve Comfort Environment

2021 ◽  
Vol 12 (3) ◽  
pp. 1409-1414
Author(s):  
A Miqdad Et.al
Keyword(s):  
Fuzzy Logic ◽  
Power Consumption ◽  
Fuzzy Logic Control ◽  
Cost Effective ◽  
Integrated System ◽  
Air Conditioner ◽  
Cooling Device ◽  
Air Conditioners ◽  
Logic Control ◽  
Internet Of Thing

Internet of Thing technology is known for its capability to collect and store a massive amount of data for further research development. Researcher widely utilizes this technology as it has become a very convenient tool with a cost-effective advantage. Practically, the calculation is made before determining the best cooling device for every room with temperature, humidity and power consumption being an important element to be taken into account. This paper present the practical approach to acquire data using WSN and IoT technology which then lead to the development of fuzzy logic control of the air conditioner with the aim to reduce its overall power consumption. Classroom equipped with two non-inverter air-conditioners with 4HP each used as a testbed.

scholarly journals Rancang Bangun Sistem Penstabil Kamera (Gimbal) dengan Logika Fuzzy untuk Pengambilan Gambar Foto dan Video

2018 ◽  
Vol 5 (3) ◽  
pp. 277
Author(s):  
Fahmizal Fahmizal ◽  
Georgius Yoga Dewantama ◽  
Donny Budi Pratama ◽  
Fahmi Fathuddin ◽  
Winarsih Winarsih
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Logic Control ◽  
Elevation Angle ◽  
Measurement Unit ◽  
Closed Loops ◽  
Good Image ◽  
Logic Control ◽  
Fuzzy Input ◽  
The Given

<p>Pada makalah ini memaparkan perancangan sistem penstabil kamera (gimbal) untuk mengurangi getaran maupun gerakan yang akan mengganggu kamera saat pengambilan gambar foto dan video. Sistem gimbal ini sangat penting digunakan dalam dunia fotografi dan videografi. Sistem gimbal yang dirancang pada penelitian ini adalah dengan  tiga buah joint pergerakan yaitu <em>roll</em>, <em>pitch</em>, <em>yaw</em> (RPY). Sensor orientasi yang digunakan pada rancangan sistem gimbal ini menggunakan sensor <em>inertia measurement unit</em> (IMU) MPU 6050 dengan <em>Kalman filter</em> (KF) sebagai pengkondisi pembacaan sudut RPY. Untuk memperoleh hasil gambar yang baik pada sistem gimbal diperlukan suatu kendali, sehingga pada penelitian ini dikembangkan suatu kendali logika <em>fuzzy</em> yang diimplementasikan dalam sebuah mikrokontroller untuk menggerakan aktuator gimbal. Sistem aktuator pada rancangan gimbal menggunakan motorservo. Nilai <em>setpoint</em> sudut gimbal yang diberikan merupakan sudut elevasi gimbal terhadap tiga sumbu sudut RPY. Selanjutnya, nilai keluaran pembacaan sensor IMU dibandingkan dengan nilai <em>setpoint </em>pada masing-masing sumbu. Setelah itu, nilai kesalahan (<em>error</em>) dan nilai perubahan kesalahan (<em>delta errror</em>) yang didapat akan digunakan sebagai nilai masukan logika <em>fuzzy</em>. Terdapat tiga buah <em>loop</em> tertutup pada kendali logika <em>fuzzy</em> untuk masing-masing sudut RPY. Hubungan relasi masukan <em>fuzzy</em> diselesaikan menggunakan aturan <em>Mamdani</em> dan keluaran dari logika <em>fuzzy</em> diselesaikan dengan menggunakan metode <em>weight average</em> (WA). Dari hasil pengujian diketahui bahwa kendali logika <em>fuzzy</em> yang diimplementasikan pada sistem gimbal mampu mengurangi efek getaran sehingga diperoleh gambar yang baik dan tidak blur.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong><em> </em></p><p class="Judul2"> </p><p><em>This paper describes the design of the camera stabilizer system (gimbal) to reduce vibration or movement that will disturb the camera when take a picture and video. This gimbal system is very important used in the world of photography and videography. Gimbal system that designed in this research is gimbal with three joints movement that is roll, pitch, yaw (RPY). The orientation sensor that used in this gimbal system design uses an inertial measurement unit sensor (IMU) MPU 6050 with Kalman filter (KF) as RPY angle reading conditioner. To obtain a good image on the gimbal system required a control, so in this research developed a fuzzy logic control that is implemented in a microcontroller to drive gimbal’s actuators. The actuators system on gimbal design uses motorservo. The given setpoint value of the gimbal is the elevation angle of gimbal against the three RPY angle axes. Furthermore, the output value of the IMU sensor is compared with the setpoint of each axis. Moreover, the error value and the change of error value (delta errror) will be used as fuzzy logic input. There are three closed loops on the fuzzy logic control for each RPY angle. The relation of fuzzy input is solved with Mamdani rule and the output of fuzzy logic is solved with weight average (WA). From the test results obtained that fuzzy logic control applied to the gimbal system is able to reduce the effects of vibration so as to obtain a good image and not blur.</em></p>

Fuzzy Control of a Mobile Robotic Vehicle

1995 ◽  
Vol 209 (2) ◽  
pp. 79-91 ◽  
Author(s):  
J W Baxter ◽  
J R Bumby
Keyword(s):  
Fuzzy Logic ◽  
Laboratory Test ◽  
Fuzzy Logic Control ◽  
Sliding Window ◽  
Test Results ◽  
Logic Control ◽  
Laboratory Test Results ◽  
Control Scheme ◽  
Robotic Vehicle ◽  
Universe Of Discourse

This paper presents a fuzzy logic control scheme for the navigation of a mobile robot in the presence of obstacles. A fuzzy navigation controller is described which guides the robot from a start position to a goal, or sub-goal, position assuming that no obstacles are in the path. Obstacles affect the navigation controller according to a set of fuzzy inhibitive rules that take into account the vehicle geometry, the distance of the obstacle from the robot and the probability of the object being at the position indicated. To ensure that all possible collision-free paths are considered, each entry in the fuzzy fit vector is distributed, or spread, across the output universe of discourse before using a sliding window defuzzification technique to produce a crisp output value. The use of the sliding window defuzzification technique helps to remove indecision from the controller. Both simulated and laboratory test results are presented.

scholarly journals Kendali Logika Fuzzy pada Car Like Mobile Robot (CLMR) Penjejak Garis

2018 ◽  
Vol 6 (3) ◽  
pp. 451
Author(s):  
FAHMIZAL FAHMIZAL ◽  
BUDI BAYU MURTI ◽  
DONNY BUDI PRATAMA ◽  
AFRIZAL MAYUB
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Pid Control ◽  
Fuzzy Logic Control ◽  
Test Results ◽  
Logic Control ◽  
Straight Line ◽  
Line Tracking ◽  
Line Trajectory ◽  
Better Than

ABSTRAKMakalah ini memaparkan perancangan sistem kendali logika fuzzy untuk mengatur kecepatan dan arah sudut steering pada car like mobile robot (CLMR) dengan menggunakan metode Ackermann steering. CLMR penjejak garis dirancang menggunakan 16 buah photodiode, dan terdapat 7 buah membership fuzzfikasi dari pembacaan error dan last error sehingga terbentuk 49 aturan. Untuk menguji perfoma kendali fuzzy pada sistem CLMR dalam mengikuti lintasan garis maka dilakukan pengujian dengan bentuk lintasan berupa garis lurus dan berbelok serta zig-zag dalam satu lintasan putar. Proses variasi nilai keanggotaan fuzzifikasi masukan dan defuzzifikasi keluaran dilakukan sebanyak lima kali. Dari hasil pengujian diperoleh bahwa kendali logika fuzzy yang diaplikasikan pada sistem mampu membuat pergerakan CLMR sukses mengikuti lintasan uji selama 9,38 detik lebih baik 0,53 detik dari kendali PID. Selanjutnya, hasil rancangan sistem CLMR ini merupakan sebuah prototipe self-driving car.Kata kunci: car like mobile robot, robot penjejak garis, fuzzy, self-driving car ABSTRACTThis paper describes the design of a fuzzy logic control system to adjust the speed and direction of the angle of the steering on the car like mobile robot (CLMR) using the Ackermann steering method. CLMR line tracking is  designed using 16 photodiode pieces, and there are 7 fuzzfication membership from reading error and last error so that 49 rules are formed. To test the fuzzy control performance on the CLMR system in following the line trajectory, it was tested with the form of a straight line and a turn and a zigzag in a rotary track. The process of varying input membership fuzzification values and output defuzzification is done five times. From the test results, it was found that the fuzzy logic control applied to the system was able to make CLMR movement successfully followed the test path for 9.38 seconds better than 0.53 seconds of PID control. Furthermore, the results of the CLMR system design are a prototype self-driving car.Keywords: car like mobile robot, line tracking robot, fuzzy, self-driving car

scholarly journals Optimasi Racing Line pada ECU (Electronic Control Unit) Mobil Listrik Berbasis Fuzzy Logic Control

2020 ◽  
Vol 8 (2) ◽  
pp. 454
Author(s):  
WIDYA CAHYADI ◽  
MUH. FAZAUDDIYAK SA’ID ◽  
ALI RIZAL CHAIDIR
Keyword(s):  
Fuzzy Logic ◽  
Electronic Control Unit ◽  
Control Process ◽  
Control Unit ◽  
Electronic Control ◽  
Maximum Acceleration ◽  
Turn Angle ◽  
Electric Cars ◽  
Speed Controller ◽  
Electric Car

ABSTRAKRacing line merupakan daerah lintasan yang berguna untuk pebalap atau pengemudi mendapatkan akselerasi maksimum. Dalam penelitian mobil listrik, racing line tidak hanya berguna untuk mendapatkan akselerasi maksimum, namun juga berguna untuk mendapatkan hasil efisiensi tertinggi terutama pada saat mobil berada di tikungan, hal ini disebabkan mobil listrik menghasilkan daya yang lebih besar pada saat berada di tikungan dibandingkan dengan lintasan lurus. Pemilihan racing line serta proses kontrol kecepatan pada mobil listrik yang baik pada tikungan bertujuan untuk mengurangi konsumsi daya pada motor serta meningkatkan hasil efisiensi mobil. Untuk dapat meningkatkan hasil efisiensi pada mobil listrik perlu ditambahkan sebuah ECU (Electronic Control Unit) dengan kontrol fuzzy logic sebagai pengendali kecepatan mobil secara otomatis pada saat mobil berada di tikungan, dengan penambahan kontrol fuzzy logic serta sensor sudut belok ini konsumsi daya yang dihasilkan oleh mobil saat berada di tikungan menjadi lebih rendah serta hasil efisiensi mobil lebih tinggi.Kata kunci: Racing Line, Sistem ECU, Logika Fuzzy ABSTRACTIn the research of electric cars, the racing line is not only useful for getting maximum acceleration, but it is also useful to get the highest efficiency results especially when the car is round the corner, this is due to electric cars producing greater power when in a curved compared to a straight track. The selection of a racing line and speed control process on a good electric car on the curved aims to reduce the power consumption of the motor and increase the efficiency of the car. To be able to increase the efficiency of the electric car, an ECU (Electronic Control Unit) is added by fuzzy logic controls as a car speed controller automatically when the car is in the corner, with the addition of fuzzy logic controls and turn-angle sensors. The car when on the curved becomes lower and the results of higher car efficiency.Keywords: Racing Line, ECU System, Fuzzy Logic

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

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