Fault detection and diagnosis (FDD) methods and fault-tolerant control (FTC) have been the focus of intensive research across various fields to ensure safe operation, reduce costs, and optimize maintenance tasks. Unmanned aerial vehicles (UAVs), particularly quadcopters or quadrotors, are often prone to faults in sensors and actuators due to their complex dynamics and exposure to various external uncertainties. In this context, this work implements different FDD approaches based on the Kalman filter (KF) for fault estimation to achieve FTC of the quadcopter, considering different faults with nonlinear behaviors and the possibility of simultaneous occurrences in actuators and sensors. Three KF approaches are considered in the analysis: linear KF, extended KF (EKF), and unscented KF (UKF), along with three-stage and adaptive variations of the KF. FDD methods, especially the adaptive filter, could enhance fault estimation performance in the scenarios considered. This led to a significant improvement in the safety and reliability of the quadcopter through the FTC architecture, as the system, which previously became unstable in the presence of faults, could maintain stable operation when subjected to uncertainties.

We have developed a double channel-cut monochromator composed of two channel-cut Si(111) crystals, and obtained highly parallel and highly monochromatized X-ray beam. Reflectivity of an As₂Se₃ film on an Al substrate was measured as a function of photon glancing angle around the critical one of total reflection. The complex refractive index was determined so that the simulated Fresnel reflectivity fits the measured values. Since the imaginary part of the refractive index is correlated with the linear absorption coefficient, we can obtain XAFS spectrum by means of reflectivity measurements in house.