The airspeed is an important feedback signal for flight control, and its measurement accuracy is related to the safety of aircraft, especially for hybrid vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAV) in the transition phase. However, offline calibration of the pitot tube cannot fully simulate the situation in real cases, and this is why online calibration after installation is necessary. In addition, the environmental wind field creates a high risk for the conversion flight of a hybrid UAV, thus real-time wind field measurement of the flight field has great significance for the flight path planning of the conversion process. In this article, an innovative method for online calibration of pitot tube and wind field estimation is proposed. After establishing the extended Kalman filter (EKF) for estimation, an analysis method is proposed to analyze the observability of EKF under different flight strategies. Then, the hovering flight is selected as the experimental flight trajectory. The laboratory computer simulation and flight experiment results validate the theory and prove that the proposed method could re-calibrate the scale factor of the pitot tube and estimate the wind field.

The integrated navigation of inertial navigation systems (INS) and the Global Positioning System (GPS) is essential for small unmanned aerial vehicles (UAVs) such as multicopters, providing steady and accurate position, velocity, and attitude information. Nevertheless, decreasing navigation accuracy is a serious threat to flight safety due to the long-term drift error of INS in the absence of GPS measurements. To bridge the GPS outage for multicopters, this paper proposes a novel navigation reconstruction method for small multicopters, which combines the vehicle dynamic model and micro-electro-mechanical system (MEMS) sensors. Firstly, an induced drag model is introduced into the dynamic model of the vehicle, and an efficient online parameter identification method is designed to estimate the model parameters quickly. Secondly, the body velocity can be calculated from the vehicle model and accelerometer measurement. In addition, the nongravitational acceleration estimated from body velocity and radar height are utilized to yield a more accurate attitude estimate. Fusing the information of the attitude, body velocity, magnetic heading, and radar height, a navigation system based on an error-state Kalman filter is reconstructed. Then, an adaptive measurement covariance algorithm based on a fuzzy logic system is designed to reduce the weight due to the disturbed acceleration. Finally, the hardware-in-loop experiment is carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed navigation reconstruction algorithm aided by the vehicle model can significantly improve navigation accuracy during a GPS outage.

Control allocation is still a challenge for tiltrotor aircraft due to effectors’ redundancy, dramatic nonlinearities and cross-coupling. Besides this, the external disturbance caused by the unique configuration of tiltrotor such as larger wing area and tiltable mechanism puts forward extra security requirement on control allocation method. Thus, this paper proposes an innovative control allocation framework from the perspective of flight safety to address this problem. Firstly, an effector distribution management scheme based on remaining control ability index (RCAI) is developed to reduce the effector saturation and ensure flight safety. Secondly, the energy consumption models for two classes of effectors: thrust vectoring and aerodynamic surfaces are introduced respectively for the subsequent optimization problem mathematically. Then, the null space transition is used to find the minimum-energy control input of effectors. Finally, key parameters of energy consumption model are identified by experiments. The real-time hardware-in-loop simulation platform is adopted to demonstrate the effectiveness of the proposed control allocation framework. Experimental results show that the energy consumption of proposed method can reduce by about 63.5% comparing to traditional control allocation method.