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Implementasi Kalman Filter Pada Kendali Roket EDF
https://doi.org/10.22146/ijeis.15436 Wisnu Pamungkas(1*), Bakhtiar Alldino Ardi Sumbodo(2), Catur Atmaji(3)
(1) Universitas Gadjah Mada
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
EDF (electric ducted fan) rocket is a flying object shapes like bullet with electric ducted fan motor as the booster. This rocket fly autonomously by utilizing accelerometer, gyroscop, and magnetometer sensor to determine the attitude of the rocket against the earth’s gravitational and magnetic field of the earth. In controlling the rocket required a control system capable of controlling a rocket with sensor data that has been processed into the value of the attitude that has been filtered.
In this study, designed a filter that will be implemented on the microcontroller rocket. The filters are Kalman filter is implemented while the control used is the control proportional integral derivative (PID) with Ziegler-Nichols tuning method.
The result of this research is an implementation of kalman filter to EDF rocket control system. Based on the experiment that has been done, control system using a Kalman filter has a standard deviation value against the value of linear regression on a roll attitude of 2.73, a pitch of 3.03, and yaw of 6.96 degrees. While the standard deviation of the ideal value on a roll attitude of 3.43, a pitch of 2.92 and yaw of 5.21 degrees.
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[1] Ardiantara, P., 2013, Purwarupa Kontrol Kestabilan Posisi dan Sikap pada Pesawat Tanpa Awak Menggunakan IMU dan Algoritma Fusion Sensor, Skripsi, MIPA, Universitas Gadjah Mada, Yogyakarta.
[2] Aydogdu, O., dan Korkmaz, M., 2012, A Simple Approach to Design of Variable Parameter Nonlinear PID Controller, Selcuk University, Department of Electrical and Electronics Engineering, Konya, Turkey.
[3] Benzerrouk, H., 2014, Modern Approaches in Nonlinear Filtering Theory Applied to Original Problems of Aerospace Integrated Navigation System with non-Gaussian Noise,. Mathematics, Saint Petersburg State University, Russia.
[4] Hutama, I., 2011, Kendali Pendulum Terbalik Dinamis, Skripsi, Teknik, Universitas Gadjah Mada, Yogyakarta.
[5] Lauszus, K., 2012, Kalman Filter Implementation for Balancing Robot.
[6] Li, Zheng, O'Doherty, Joseph E., Hanson, Timothy L., Lebedev, Mikhail A., Henriquez, Craig S., dan Nicolelis, Miguel A. L., 2009, Unscented Kalman Filter for Brain-Machine Interfaces, Duke University Graduate School, Natal, Brazil.
[7] Marins J., Yun, X., Bachman, E., McGhee, R., dan Zyda, M., 2001, An Extended Kalman Filter For Quarternion-Based Orientatin Estimation Using MARG Sensors, International Coference on Intellegent Robots and System, 3, 1.
[8] McCarron, B., 2013, Low-Cost IMU Implemenetation via Sensor Fusion Algorithms in the Arduino Environment, Senior Project. Faculty of Aerospace Engineering Departmen, California Polytchnic State University, San Luis Obispo.
[9] Sabatini, Angelo Maria, 2011, Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation, BioRobotics Institute, Pisa, 11.
[10] Tusell, Fernando, 2011, Kalman Filtering in R, University of the Basque Country, Bilbao, 39, 2.
DOI: https://doi.org/10.22146/ijeis.15436
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