İHA tutum tahmini için yeni bir bulanık mantık tabanlı uyarlanabilir tamamlayıcı filtre algoritması

Öz

Mikro Elektro-Mekanik Sistem (MEMS) Tabanlı Atalet Ölçüm Birimleri (IMU), küçük, hafif ve maliyet etkinliği nedeniyle insansız hava aracı (İHA) sistemlerinde tutum tahmini için yaygın olarak kullanılmaktadır. Öte yandan, gürültülü çıkış, düşük hassasiyet, zayıf doğruluk ve önyargı kararlılığı gibi performansı etkileyen bazı dezavantajları vardır. Ayrıca, MEMS tabanlı IMU sensörleri (ivmeölçerler ve manyetometreler ve jiroskoplar) bağımsız bir sistem olarak yeterli navigasyon çözümleri sağlayamaz. Güvenilir tutum tahmini elde etmek için literatürde farklı sensör füzyon teknikleri önerilmiştir. Ancak, bunların çoğu, doğrusal olmayan ölçüm modelleri, doğrusal olmayan süreç dinamikleri ve uzun menzilli gezinme gibi durumlarda başarısız olur. Bu çalışma, dinamik hareket altındaki bir İHA'da daha gürbüz bir tutum tahmini başarmak için düşük maliyetli MEMS tabanlı IMU sensörlerinden alınan manyetik alan, açısal hız ve ivme ölçümlerini birleştiren yeni bir bulanık kural tabanlı tamamlayıcı filtre sunmaktadır. Önerilen yaklaşım, sistemin değişken dinamik hareketine göre tamamlayıcı filtrenin kesme frekansını optimum değere ayarlar. Böylece sabit kesme frekansı sorunu ortadan kaldırılır ve sistemin değişen hareketlerinde bile daha sağlam bir tutum tahmini elde edilir. Hem gerçek deneyler hem de sayısal simülasyonlar, sunulan yöntemin geçerliliğini doğrulamaktadır.

Anahtar Kelimeler

• Tutum tahmini
• Atalet ölçüm birimi
• Bulanık mantık
• Tamamlayıcı filtre
• Gerçek zamanlı deneyler

A new fuzzy logic-based adaptive complementary filter algorithm for UAV attitude estimation

Abstract

Micro Electro-Mechanical System (MEMS) Based Inertial Measurement Units (IMU) are widely used for attitude estimation in unmanned aerial vehicle (UAV) systems owing to their small, light weight and cost effectiveness. On the other hand, it has some disadvantages that influence performance, such as noisy output, low sensitivity, poor accuracy, and bias stability. Also, MEMS-based IMU sensors (accelerometers and magnetometers and gyroscopes) cannot provide adequate navigation solutions as a standalone system. Different sensor fusion techniques have been proposed in the literature to obtain reliable attitude estimation. However, most of these fail in situations such as nonlinear measurement models, nonlinear process dynamics, and long-range navigation. This article presents a new fuzzy rule-based complementary filter (CF) that combines magnetic field, angular velocity and acceleration measurements from low-cost MEMS-based IMU sensors to achieve a more robust attitude estimation in a UAV under dynamic motion. The proposed approach adjusts the cut-off frequency of the CF to the optimum value according to the variable dynamic motion of the system. Thus, the problem of constant cut-off frequency is eliminated and a more robust attitude estimation is achieved even with the varying movements of the system. Both real experiments and numerical simulations confirm the validity of the presented method.

Keywords

• Attitude estimation
• Inertial measurement unit
• Fuzzy Logic
• Complementary filter
• Real time experiments

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