In this work we present a novel, approximate, efficient algorithm for determining the Trim Flight Envelope of a fixed-wing UAV, based on a generic, nonlinear numerical model. The resulting Flight Envelope is expressed as a convex intersection of half-spaces. Subsequently, a Model Predictive Controller (MPC) is designed which takes into account the Flight Envelope constraints, to avoid Loss-of-Control. The overall system is shown to operate in real-time in a simulation environment.

MAVLink is a popular message protocol for small Unmanned Aerial Vehicles (UAVs). In this work, we present a Fault Detection and Isolation (FDI) framework for fixed-wing UAVs which takes advantage of the information conveyed in MAVLink telemetry streams and produces a bank of residual generators. Structural Analysis is employed to systematically handle the varying set of available measurements, identify the observable faults and adjust the FDI system accordingly. Structural detectability and isolability analyses are carried out. A case-study on a real-life telemetry log of a UAV crash demonstrates the efficacy of the proposed approach.

Parity-Based methodologies for fault diagnosis in UAVs often result in nonlinear residual generators. Still, a systematic framework to perform detectability and robustness analyses of residual generators does not exist. In this work, detectability and robustness metrics for static and dynamic residuals are presented, while numerical methods, specifically Particle Swarm Optimization, are employed to calculate them. The results are used to characterize the performance of a fault detection system. An application on a UAV model is shown, based on real flight data.

This paper focuses on real-time estimation of the aerodynamic model parameters of small-scale fixed wing Unmanned Aerial Vehicles (UAVs) without the aid of wind-tunnel experiments, using exclusively flight data. The key tool of the following analysis centers around the principles of Total Least Squares estimation. Contrary to Ordinary Least Squares, this method accounts for errors in both explanatory data and variables to-be-explained. This is a highly desirable property for UAVs equipped with low-cost sensor systems. The proposed implementation combines both batch and real-time schemes, while deals efficiently with the problem of Insufficient System Excitation. Online adaptation to model changes is performed by applying a Variable Forgetting Factor to the estimation data. Finally, a Monte Carlo approach is developed for uncertainty estimation regarding compound aerodynamic variables.

Structural Analysis is a lucrative option for Fault Detection and Identification in Unmanned Aerial Vehicles (UAVs), because it handles detailed, large-scale mathematical models. It can be employed by an on-board flight computer to generate residual generators and implement automatic fault-detection. Contemporary algorithms applied on dynamic systems may yield residual generators which require the real-time solution of Differential-Algebraic Equation (DAE) systems. Depending on the form and differential index of each DAE system, its solution may not be possible exclusively by computational means. In this paper we explore the relation between Structural Analysis algorithms and the forms of DAE systems they produce, propose conditions under which all generated DAEs are Structural Index-1 and semi-explicit and provide a large-scale fixed-wing UAV model with that property.