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Unmanned aerial helicopters are essential for use in environments that are inaccessible for fixed wing aerial vehicles. Flybarless helicopters are famous for their high agility and maneuverability, which makes them suitable platforms in many challenging applications. This paper is concerned with the problem of estimating attitude and flapping angles of a flybarless, small-scale, single-rotor helicopter. The study utilizes a nonlinear model for the Maxi Joker 3 helicopter. A dynamic model-based Kalman filter is designed and implemented to estimate both the attitude and the flapping angles of the helicopter. Results of a simulation scenario are shown to validate the performance of the proposed approach. The results demonstrate high-accuracy flapping angles estimation with errors not exceeding,│Δ max │,0.3° in longitudinal flapping angles and 0.1° in lateral flapping angles. An experimental test is also conducted to demonstrate the performance of the method.

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This paper presents a new approach to estimate the orientation of a quadrotor using single low-cost inertial measurement unit (IMU) sensor. The proposed hybrid solution uses two extended Kalman filters (EKF) along with a direction cosine matrix (DCM) algorithm. An EKF utilizes the dynamics of the quadrotor to filter the noise on the body-frame (B-frame) angular rates measured by the three-axis gyroscope sensor. Then, a DCM algorithm uses the filtered gyro signal along with the reading from a three-axis accelerometer and a three-axis magnetometer sensor to estimate the Euler angles. Finally, an additional EKF is presented to enhance the final estimates of the Euler angles. The performance of the proposed hybrid approach is tested and compared with other commonly used methods. Results are presented at the end of the paper to verify the performance of the proposed method. The results show an improvement in the estimated quadrotor's state. Monte Carlo tests are performed to ensure sustained high accuracy estimation.