Öz
Kaynakça
- S. O. Madgwick, A. J. Harrison, R. Vaidyanathan. "Estimation of IMU and MARG orientation using a gradient descent algorithm." 2011 IEEE international conference on rehabilitation robotics. IEEE, 2011.
- N. Ahmad, R. A. R. Ghazilla, N. M. Khairi & V. Kasi, (2013). Reviews on various inertial measurement unit (IMU) sensor applications. International Journal of Signal Processing Systems, 1(2), 256-262.
- M.Pedley,(2013). Tilt sensing using a three-axis accelerometer. Freescale semiconductor application note, 1, 2012-2013.
- Y. Guan, & X. Song, (2018). Sensor fusion of gyroscope and accelerometer for low-cost attitude determination system. In 2018 Chinese Automation Congress (CAC) (pp. 1068-1072). IEEE.
- D. Q. Duong, J. Sun, T. P. Nguyen, & L. Luo, (2016, August). Attitude estimation by using MEMS IMU with fuzzy tuned complementary filter. In 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT) (pp. 372-378). IEEE.
- P. Gui, L. Tang, & S. Mukhopadhyay, (2015, June). MEMS based IMU for tilting measurement: Comparison of complementary and kalmanfilter based data fusion. In 2015 IEEE 10th conference on Industrial Electronics and Applications (ICIEA) (pp. 2004-2009). IEEE.
- X. Shen, M. Yao, W. Jia, & D. Yuan, (2012). Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm. Measurement, 45(5), 1257-1265.
- W. Elmenreich, (2002). Sensor fusion in time-triggered systems (Doctoral dissertation).
- . Colton, & F. R. C. Mentor, (2007). The balance filter. Presentation, Massachusetts Institute of Technology.
Öz
The use of unmanned aerial vehicles (UAV) systems has increased in recent years. Therefore,studies on UAVs have increased today. In this direction, the production of UAV systems with domestic resources has gained importance. In this study, it is desired to develop a domestic and national flight control card and software. In the flight control board designed for the UAV, it is aimed to keep the vehicle in balance in the air. Accurate measurement of platform orientation plays an important role in many applications such as aerospace, robotics, navigation, marine, machine interaction [1]. Inertial Measurement Unit (IMU) sensor was used to accurately measure the orientation of the UAV. IMU sensor is widely used in UAVs due to its light weight and low energy consumption. In this direction, the need for a filter has emerged in the IMU sensor, which is used to accurately measure the orientation of the unmanned aerial vehicle. In this study, a complementary filter was applied on the IMU sensor. Thanks to this filter, it has been observed that the accuracy of the data received from the IMU sensor has increased. Based on the data obtained, a Proportional Integral Derivative (PID) algorithm was developed, and the vehicle was kept in balance. In this study, ARMCortex-M4 based STM32F407VG microcontroller and MPU6050 as IMU sensor were used. Keil-uVision5 compiler is preferred for software. As a result, high accuracy in the orientation detection of unmanned aerial vehicles was obtained by applying a complementary filter on the IMU sensor.
Anahtar Kelimeler
Unmanned Aerial Vehicles STM32F407VG IMU Complementary Filter PID
Kaynakça
- S. O. Madgwick, A. J. Harrison, R. Vaidyanathan. "Estimation of IMU and MARG orientation using a gradient descent algorithm." 2011 IEEE international conference on rehabilitation robotics. IEEE, 2011.
- N. Ahmad, R. A. R. Ghazilla, N. M. Khairi & V. Kasi, (2013). Reviews on various inertial measurement unit (IMU) sensor applications. International Journal of Signal Processing Systems, 1(2), 256-262.
- M.Pedley,(2013). Tilt sensing using a three-axis accelerometer. Freescale semiconductor application note, 1, 2012-2013.
- Y. Guan, & X. Song, (2018). Sensor fusion of gyroscope and accelerometer for low-cost attitude determination system. In 2018 Chinese Automation Congress (CAC) (pp. 1068-1072). IEEE.
- D. Q. Duong, J. Sun, T. P. Nguyen, & L. Luo, (2016, August). Attitude estimation by using MEMS IMU with fuzzy tuned complementary filter. In 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT) (pp. 372-378). IEEE.
- P. Gui, L. Tang, & S. Mukhopadhyay, (2015, June). MEMS based IMU for tilting measurement: Comparison of complementary and kalmanfilter based data fusion. In 2015 IEEE 10th conference on Industrial Electronics and Applications (ICIEA) (pp. 2004-2009). IEEE.
- X. Shen, M. Yao, W. Jia, & D. Yuan, (2012). Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm. Measurement, 45(5), 1257-1265.
- W. Elmenreich, (2002). Sensor fusion in time-triggered systems (Doctoral dissertation).
- . Colton, & F. R. C. Mentor, (2007). The balance filter. Presentation, Massachusetts Institute of Technology.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Matematik |
| Bölüm | Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Ağustos 2022 |
| Gönderilme Tarihi | 4 Haziran 2022 |
| Kabul Tarihi | 21 Haziran 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 06 Sayı: 1 |
Kaynak Göster
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