Real-time parameter identification method for a novel blended-wing-body tiltrotor UAV

An accurate aircraft dynamic model is essential for the flight control system design and flight characteristic analysis of tiltrotor unmanned aerial vehicle (UAV). The unique aerodynamic interaction between the rotor and wing and the installation misalignment angles of rotor could obviously degrade the control performance of flight. However, the traditional test-bed-based identification method cannot fully simulate the real flying conditions. Thus, a real-time method based on the Error-State Kalman Filter (ESKF) is proposed to estimate the model parameters of the tiltrotor UAV. Based on the navigation information from the onboard integrated navigation system, the model parameters are augmented into the aircraft dynamic model to be estimated. Then, the observability analysis is carried out to demonstrate the convergence of the proposed method and provide the proper measurements from which the model parameters possess the best observable degree. Finally, both simulations and real flight experiments are performed to validate the effectiveness of the proposed method.

Automated summary

An accurate aircraft dynamic model is crucial for the design of flight control system and analysis of flight characteristics for tiltrotor UAV. The traditional test-bed identification method is insufficient to simulate real flying conditions, hence a real-time method based on Error-State Kalman Filter is proposed to estimate the model parameters. Observability analysis is conducted to ensure convergence of the proposed method, and both simulations and real flight experiments demonstrate the effectiveness of the method.