Bir mekanik jiroskopun yalpalama tahmininde uzun kısa süreli bellek ağı yaklaşımı

Year 2023, Volume: 15 Issue: 3, 179 - 193, 31.12.2023

İlyas Kacar

https://doi.org/10.29137/umagd.1293684

Abstract

Mekanik jiroskoplar, ürettikleri jiroskopik tork sayesinde hava ve uzay araçları gibi tamamen asılı duran araçlara yön vermek için kullanılmaktadır. Kara araçlarında da tek veya iki tekerlekli araçların otonom dengesi için kullanılmaktadır. Her ne kadar uzun yıllardır regresyonlar mevcut veriyi modelleme amaçlı kullanılagelen bir yöntem olmuşsa da son yıllarda makine ve derin öğrenme yöntemlerinin sınıflama, modelleme konularında yüksek başarıya sahip oldukları görülmüştür. Bu çalışmada bir derin öğrenme yöntemi olan uzun kısa süreli bellek ağı kullanılarak mekanik bir jiroskopun yalpalama hızı tahmin edilmiştir. Elde edilen modelde RMSE 0.0055 rad/s iken ME değeri -0.0012 rad/s ve R 0.9998 olup model çıktısı ile veri seti arasında yüksek ilişki mevcuttur. Bu hali ile derin öğrenme yöntemlerinin mekanik jiroskop tasarımlarında kullanılması fayda sağlayacaktır.

Keywords

Jiroskop, Makine öğrenimi, Uzun kısa süreli hafıza

Thanks

Rijit dinamik simülasyonları için kullanılan Ansys®’in eğitsel amaçlı kullanım imkânını sağlayan Karadeniz Teknik Üniversitesi’ne, Dr. Mehmet Seyhan’a teşekkür ederim.

Long-short-term memory network approach to forecast the precession of a mechanical gyroscope

Abstract

Mechanical gyroscopes are used to orient the fully suspended vehicles such as air and space vehicles, thanks to the gyroscopic torque they produce. It is also used in land vehicles for the autonomous balance of single or two-wheeled vehicles. Although regressions have been used for modeling existing data for many years, it has been seen that machine and deep learning methods have higher success in classification and modeling in recent years. In this study, the precession of a mechanical gyroscope was forecasted by using a long-short-term memory network, which is a deep learning method. In the model obtained, the RMSE is 0.0055 rad/s, the ME value is -0.0012 rad/s, and the R is 0.9998, and there is a high correlation between the model output and the dataset. So, it is useful to use deep learning methods in mechanical gyroscope designs.

Keywords

Gyroscope, Machine Learning, Long short-term memory

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