Bir dönerkanadın UKF tabanlı durum kestirimi

Döner kanatlar, havada asılı kalabilme, dikey iniş kalkış yapabilme ve düşük hızlarda uçabilme özelliklerine sahip iyi bilinen bir insansız hava aracı türüdür. Fakat, döner kanatlar hatalı durum kestirimine yol açan modelleme ve sensör hatalarına maruz kalmaktadırlar. Kalman filtresi, Gauss dağılmış rassal gürültüye sahip doğrusal sistemler için eniyiliği kanıtlanmış bir filtredir. Bununla birlikte, durum değişkenleri arasındaki etkiler ve doğrusallıktan uzak denklemlerden dolayı döner kanatların doğrusal modellenmesi gerçeği yansıtmamaktadır. EKF bu sorunu çözmek için önerilen bir filtredir. Fakat, doğrusal olmayan sistemlerin birinci dereceden Taylor serisi ile yaklaşımı yetersiz kalabilmektedir. Bu sebeple, UT ye dayanan başka bir Kalman filtresi önerilmiştir. UKF, durum dağılımını Gauss rassal değişkeni olarak üçüncü dereceden herhangi doğrusal olmayan durum için modelleyebilmektedir. Bu çalışmada, doğrusal olmayan döner kanat durum dinamikleri için bahsedilen ölçüm ve modelleme hatalarına çözüm amaçlı UKF tabanlı kestirimci gerçeklenmiştir. İstatistiksel analiz sonuçlarına göre, yaklaşım gürültüleri kestirebilmekte ve MAE, RMSE hataları ile gürültü varyansını tüm durum değişkenleri için düşürebilmektedir. Bu çalışma Eskişehir Teknik Üniversitesi Bilimsel Araştırma Projeleri Komisyonu tarafından kabul edilen 20ADP229 nolu proje kapsamında desteklenmiştir.

Unscented Kalman filter based attitude estimation of a quadrotor

Quadrotors are well - known unmanned aerial vehicle structures that have some advantages such as hovering, vertical take – off and landing, and low – speed flight. On the other hand, quadrotors are subjected to modeling and sensor uncertainties that lead to erroneous state estimation. Kalman filter has been proven to be the optimal estimator for the Gaussian distributed noise for linear processes. However, linear dynamical models of the quadrotors are not accurate representations of the systems due to nonlinearities, and coupling between the states. Extended Kalman filter (EKF) is proposed to solve the above issue. But, first order Taylor series approximation for the nonlinear state model may lead inefficiencies. For this reason, another Kalman filter framework is proposed that employs unscented transformation (UT). Unscented Kalman filter (UKF), can model the state distribution as Gaussian random variable to the third degree for arbitrary nonlinearities. So, in this study, unscented Kalman filter based estimation scheme is presented to overcome the sensor and model noises for nonlinear quadrotor attitude dynamics. According to the statistical analysis, the approach can estimate and reduce the mean absolute error, root mean square error and also variance of the noise for all attitude states. This study was supported by Eskişehir Technical University Scientific Research Projects Commission under the grant no: 20ADP229.

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