Implementasi Sistem Kendali PID pada Gimbal Kamera 2-sumbu dengan Aktuator Motor Brushless

Priyambodo, Tri Kuntoro

doi:10.22146/ijeis.18238

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…[1] Gimbal saat ini banyak diaplikasikan pada beberapa bidang seperti bidang navigasi (avionics), videografi maupun photografi. [2] Hasil gambar dan video dapat dipengaruhi oleh berbagai macam gangguan sehingga menghasilkan gambar dan video yang kurang fokus dan terjadi efek blur atau bisa juga menyebabkan hasil video menjadi berguncang dan tidak stabil. [3] Dalam bidang photografi dan videografi, gimbal digunakan untuk menjaga dan mempertahankan posisi kamera pada sudut tertentu sehingga kamera dapat menghasilkan gambar dengan kesan yang lebih halus.…”

Section: Pendahuluanunclassified

Emisi Radiasi Stabilizer Gimbal Genggam Pada Frekuensi 30 MHz – 1 GHz Dan 1 – 6 GHz

Wirapraja¹

2020

Jur. Tek. Pros. & Inov. Ind.

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Section: Pendahuluanunclassified

Emisi Radiasi Stabilizer Gimbal Genggam Pada Frekuensi 30 MHz – 1 GHz Dan 1 – 6 GHz

Wirapraja¹

2020

Jur. Tek. Pros. & Inov. Ind.

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“…Penelitian yang dilakukan oleh (Priyambodo, 2017) memanfaatkan gimbal dengan dua sumbu gerak untuk rancangan sistem penstabil kamera. Pada penelitian tersebut, sistem kendali yang diterapkan menggunakan PID kontroler dengan tuning Ziegler-Nichols.…”

Section: Pendahuluanunclassified

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

Fahmizal

Dewantama

Pratama

et al. 2018

JTIIK

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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 roll, pitch, yaw (RPY). Sensor orientasi yang digunakan pada rancangan sistem gimbal ini menggunakan sensor inertia measurement unit (IMU) MPU 6050 dengan Kalman filter (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 fuzzy yang diimplementasikan dalam sebuah mikrokontroller untuk menggerakan aktuator gimbal. Sistem aktuator pada rancangan gimbal menggunakan motorservo. Nilai setpoint sudut gimbal yang diberikan merupakan sudut elevasi gimbal terhadap tiga sumbu sudut RPY. Selanjutnya, nilai keluaran pembacaan sensor IMU dibandingkan dengan nilai setpoint pada masing-masing sumbu. Setelah itu, nilai kesalahan (error) dan nilai perubahan kesalahan (delta errror) yang didapat akan digunakan sebagai nilai masukan logika fuzzy. Terdapat tiga buah loop tertutup pada kendali logika fuzzy untuk masing-masing sudut RPY. Hubungan relasi masukan fuzzy diselesaikan menggunakan aturan Mamdani dan keluaran dari logika fuzzy diselesaikan dengan menggunakan metode weight average (WA). Dari hasil pengujian diketahui bahwa kendali logika fuzzy yang diimplementasikan pada sistem gimbal mampu mengurangi efek getaran sehingga diperoleh gambar yang baik dan tidak blur. Abstract 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.

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“…This problem has been studied before using various methods such as Brushless DC motor speed control with PID method and received a speed response of 0.7 seconds. This study was not tested when the BLDC motor was in load state [6], BLDC motor speed control with artificial neural network method and received a response of 0.84 seconds. This study was not tested when the BLDC motor was in load state [7], designing BLDC motor controller with fuzzy model reference learning control (FMRLC) method and getting 7.02 seconds response.…”

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confidence: 99%

Simulation of Brushless DC Motor Speed Control with Fuzzy Logic Method

Ginola¹,

Pulungan²,

Purwanto³

et al. 2020

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Brushless DC Motor is used frequently in industrial applications, because this type of motor has high efficiency and does not cause sparks. However, the speed control of this motor generally has a slow response to the set point and the speed is not constant when given different loads. Therefore, it is essential to design sophisticated speed control system for this motor to improve the constant speed and speed response. In this paper, fuzzy logic method for speed control system is used to give the decision of how much voltage will be added to the motor to get to the desired speed set point value, and how much voltage will be reduced if the speed of the motor passes through the speed set point. brushless dc motor speed is set with input voltage to the motor taking into account speed error and delta speed error using Fuzzy Logic, so that when the speed of the motor almost reaches the set point the input voltage to the motor is reduced so as not to overshoot until the speed of the motor reaches the set point. The results of this study gave a better response than not using fuzzy logic methods. Where at set point 270 gets response time to set point (Tr) faster 0.0002 seconds from 0.0046 seconds to 0.0044, the time to steady state (Ts) is faster 0.025 seconds from 0.04 seconds to 0.015 seconds, the maximum pass percentage (Mp) is reduced from 75% to 20%, and error speed from 1.5% to 0%. And for a trial load at t=0.025 seconds in set point 270 with a load of 4Nm responds to the set point back for 0.014 seconds.

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Implementasi Sistem Kendali PID pada Gimbal Kamera 2-sumbu dengan Aktuator Motor Brushless

Abstract: Abstrak

Cited by 4 publications

References 13 publications

Emisi Radiasi Stabilizer Gimbal Genggam Pada Frekuensi 30 MHz – 1 GHz Dan 1 – 6 GHz

Emisi Radiasi Stabilizer Gimbal Genggam Pada Frekuensi 30 MHz – 1 GHz Dan 1 – 6 GHz

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

Simulation of Brushless DC Motor Speed Control with Fuzzy Logic Method

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