From fault-tolerant control to failure detection, blade damage simulation is integral for developing and testing failure-resilient modern unmanned aerial vehicles. Existing approaches assume partial loss of rotor effectiveness or reduce the problem to centrifugal forces resulting from the shift in the propeller centre of gravity. In this study, a white-box blade damage model based on Blade Element Theory is proposed, integrating both mass and aerodynamic effects of blade damage. The model serves as plug-in to the nominal system model, enables the simulation of any degree of blade damage and does not require costly experimental data from failure cases. A complementary methodology for the identification of the airfoil lift and drag coefficients is also presented. Both contributions were demonstrated with the Bebop 2 drone platform and validated with static test stand wrench measurements obtained at 3 levels of blade damage (0%, 10%, 25%) in a dedicated wind tunnel experimental campaign with velocities up to 12 m/s. Results indicate high accuracy in simulating a healthy propeller. In the presence of blade damage, the model exhibits a relative error between 5% and 24% at high propeller rotational speeds and between 15% and 75% at low propeller rotational speeds.@en

Online fault detection and diagnosis (FDD) enables Unmanned Aerial Vehicles (UAVs) to take informed decisions upon actuator failure during flight, adapting their control strategy or deploying emergency systems. Despite the camera being a ubiquitous sensor on-board of most commercial UAVs, it has not been used within FDD systems before, mainly due to the nonexistence of UAV multi-sensor datasets that include actuator failure scenarios. This paper presents a knowledge-based FDD framework based on a lightweight LSTM network and a single layer neural network classifier that fuses camera and Inertial Measurement Unit (IMU) information. Camera data are pre-processed by first computing its optical flow with RAFT-S, a state-of-the-art deep learning model, and then extracting features with the backbone of MobileNetV3-S. Short-Time Fourier Transform is applied on the IMU data for obtaining their time-frequency information. For training and assessing the proposed framework, UUFOSim was developed: an Unreal Engine-based simulator built on AirSim that allows the collection of high-fidelity photo-realistic camera and sensor information, and the injection of actuator failures during flight. Data were collected in simulation for the Bebop 2 UAV with 16 failure cases. Results demonstrate the added value of the camera and the complementary nature of both sensors with failure detection and diagnosis accuracies of 99.98% and 98.86%, respectively.@en

Linear Approximate Dynamic Programming (LADP) and Incremental Approximate Dynamic Programming (IADP) are Reinforcement Learning methods that seek to contribute to the field of Adaptive Flight Control. This paper assesses their performance and convergence, as well as the impact of sensor noise on policy convergence, online system identification, performance and control surface deflection. After summarising their theory and derivation with full state (FS) and output feedback (OPFB), they are implemented on the linearised longitudinal F16 model. In order to establish an objective performance comparison, their hyper-parameters were tuned with an evolutionary algorithm: Particle Swarm Optimisation (PSO). Results show that LADP and IADP have the same performance in the presence of FS feedback, whereas LADP outperforms IADP when only OPFB is available. Output noise causes LADP based on OPFB to diverge. In the case of IADP based on OPFB, sensor noise improves the performance due to a better exploration of the solution space. The present research aims at bridging the gap between the discussed ADP algorithms and real world systems.

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