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.

Micro Aerial Vehicles (MAV) have been seen rapid progress in the indoor entertaining, security monitoring, as well as search and rescue activities. The indoor localization with lightweight sensors in a Global Positioning System (GPS-denied) environment is a challenging topic for MAVs autonomous flight and path planning. This paper proposes a novel indoor localization approach relying on only the IMU and four ultrasonic sensors. Four mutually perpendicular installed ultrasonic sensors are used to provide distances of each direction. A prior map and an improved multiple rays model are constructed to approximate the measurement of the ultrasonic sensor. A fast algorithm to calculate the Jacobian matrix of the measurement function is given, then an Extended Kalman Filter (EKF) is conducted to fuse the information from IMU and the sonar sensor. The proposed algorithm is validated by the simulation and the results indicate good localization performance and robustness against compass measurement noise.

Considering the strong uncertainties of aerodynamic parameters and atmospheric environment and the large dynamic variation during the Reusable Launch Vehicle (RLV) reentry process, this paper introduces an attitude controller design method which combines Linear Quadratic Regulator (LQR) with Fuzzy Gain-Scheduling (FGS). The whole operating envelope is divided into several design points firstly. And then, control state deviation based LQR controllers are designed for design points respectively. Thereafter the FGS controller is obtained by the use of Takagi-Sugeno (TS) fuzzy model. In the next step, the 6-DoF nonlinear simulation conveys that the stability of off-design points is guaranteed and the proposed controller is capable of maintaining a satisfactory tracking performance under uncertainties among the operating envelope.