Due to its payload, size and computational limits, localizing a micro air vehicle (MAV) using only its onboard sensors in an indoor environment is a challenging problem in practice. This paper introduces an indoor localization approach that relies on only the inertial measurement unit (IMU) and four ultrasonic sensors. Specifically, a novel multi-ray ultrasonic sensor model is proposed to provide a rapid and accurate approximation of the complex beam pattern of the ultrasonic sensors. A fast algorithm for calculating the Jacobian matrix of the measurement function is presented, and then an extended Kalman filter (EKF) is used to fuse the information from the ultrasonic sensors and the IMU. A test based on a MaxSonar MB1222 sensor demonstrates the accuracy of the model, and a simulation and experiment based on the $ThalesII$ MAV platform are conducted. The results indicate good localization performance and robustness against measurement noises.

The goal of this research is to present a discrete extended state observer that can effectively track fast time-varying disturbances while maintaining low sensitivity to measurement noise. A novel disturbance estimation method, named the discrete bi-bandwidth extended state observer (DBESO), is proposed. The DBESO employs a bi-bandwidth gain pencil, which comprises a base bandwidth and a bi-bandwidth scaling factor, to adjust the bandwidth of the observer. Consequently, DBESO can achieves a better tradeoff between the disturbance estimation accuracy and noise suppression. Specific details of DBESO, including its general form and stability proof, are discussed. Furthermore, we proposed three bi-bandwidth gain pencil design methods. The simulation comparisons reveal that the smooth gain pencil exhibits superior estimation performance. Finally, we integrated the observer with PD control and applied it to the rudder deflection control platform. Moreover, the experimental results demonstrate its effectiveness in compensating for fast time-varying disturbances and suppressing measurement noise.

Owing to the complexity of aircraft structural damage, directly measuring the pattern and severity of damage using onboard sensors is difficult. This paper proposes a linear parameter-varying (LPV) model–based online estimation and indirect adaptive control scheme for aircraft with severe structural damage. Specifically, the unmeasurable damage parameters are designed as the gain scheduling coefficients (GSCs) of the LPV model. By employing the proposed LPV model, the online computational load is significantly reduced compared with that of typical existing methods. Furthermore, an online identification algorithm with a Lyapunov stability guarantee is presented to estimate the GSCs, and an online-designed model reference controller that combines a state-feedback decoupling controller and a disturbance rejection term is adopted to implement attitude control. The closed-loop system is simulated with the National Aeronautics and Space Administration (NASA) generic transport model under a left wing tip damage scenario. The simulation results demonstrate that the proposed method can estimate GSCs and the uncertainty of the aircraft rapidly and accurately, and the closed-loop system precisely achieves the desired attitude responses.