Open AccessThe UKF Based Approach to Improving Attitude and Position of Quadcopter Through Autonomous and Non-Autonomous Flight
Author(s) -
Ahmed Ibraheem Abdulkareem,
Basil H. Jasim,
Safanah M. Raafat
Publication year - 2020
Publication title -
iraqi journal for electrical and electronic engineering/al-maǧallaẗ al-ʻirāqiyyaẗ al-handasaẗ al-kahrabāʼiyyaẗ wa-al-ilikttrūniyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2078-6069
pISSN - 1814-5892
DOI - 10.37917/ijeee.16.2.5
Subject(s) - quadcopter , gyroscope , control theory (sociology) , extended kalman filter , kalman filter , noise (video) , accelerometer , angular displacement , angular velocity , computer science , nonlinear system , attitude control , filter (signal processing) , fault detection and isolation , control engineering , engineering , artificial intelligence , computer vision , acoustics , actuator , aerospace engineering , physics , control (management) , quantum mechanics , image (mathematics) , operating system
The gyroscope and accelerometer are the basic sensors used by most Unmanned Aerial Vehicle (UAV) like quadcopter to control itself. In this paper, the fault detection of measured angular and linear states by gyroscope and accelerometer sensors are present. Uncertainties in measurement and physical sensors itself are the main reasons that lead to generate noise and cause the fault in measured states. Most previous solutions are process angular or linear states to improving the performance of quadcopter. Also, in most of the previous solutions, KF and EKF filters are used, which are inefficient in dealing with high nonlinearity systems such as quadcopter. The proposed algorithm is developed by the robust nonlinear filter, Unscented Kalman Filter (UKF), as an angular and linear estimation filter. Simulation results show that the proposed algorithm is efficient to decrease the effect of sensors noise and estimate accurate angular and linear states. Also, improving the stability and performance properties of the quadcopter. In addition, the new algorithm leads to increasing the range of nonlinearity movements that quadcopter can perform it.