Fixed-wing Unmanned Aerial Vehicles (UAVs) are a special class of UAVs which present many advantages notably long range of action. Whereas, design of this kind of UAVs requires heavy logistics like outdoor tests, runways, and experimented pilots. These constraints reverberate on the design of embedded systems for fixed-wing UAVs. Because static tests are not representative, this paper proposes a practical approach to evaluate an embedded system on an appropriate vehicle emulating the dynamic model of a fixed-wing aircraft. For that, a comparison between the dynamic model of fixed-wing aircraft, tank-type mobile robot, and a bicycle is achieved. We show that, contrary to trend in literature, a mobile robot is not the optimal choice to emulate a fixed-wing UAV. Indeed, supposing a motion without slip (and a constant altitude for the aircraft), translation models of the three vehicles are under the form of Dubin car model. Whereas, translation and rotation velocities of tank-type mobile robot are coupled (while it is not the case for the aircraft where propulsion and turning are actuated separately). This constraint defines an allowed kinematic zone which limits the emulation of a fixed wing airplane. In the other hand, in bicycle model “bank to turn effect” is similar to the one observed in fixed-wing aircraft model. Furthermore, both models are not defined when the translation velocity tends to zero (stalling effect). As a conclusion, we propose to use mobile robot to test the navigation layer, and the bicycle to evaluate the sensor processing layer of an embedded system based fixed-wing UAVs applications.

Unmanned Aerial Vehicles (UAVs) are aeronautical systems that must meet stringent safety and reliability requirements. Therefore, the lifecycle management of UAVs should follow well-defined standards and methodologies. Although upgrades and modernisation are critical phases in the UAV lifecycle, there is a lack of studies addressing the challenges associated with them. This article presents a model-based methodology for guiding upgrade and modernisation efforts aimed at enhancing UAV capabilities while mitigating technical and operational risks. As a case study, we apply this methodology to upgrade the propulsion and control systems of a fixed-wing UAV by replacing the piston engine with a Brushless Direct Current (BLDC) motor and enhancing control accuracy by adopting a Sugeno Fuzzy Logic Controller (S-FLC) instead of the original PID-based control architecture. We conduct a thorough evaluation of the existing system, including dynamic modelling of the UAV and an examination of its Guidance-Navigation-Control (GNC) system. We then model both the original piston engine and the BLDC motor to assess their performance. An optimisation strategy is introduced to improve the fuzzy controller’s accuracy while minimising energy consumption. To strike a balance between these competing objectives, the optimisation strategy incorporates a multi-objective optimisation phase. The optimisation strategy utilises both the Genetic Algorithm (GA) and the Multi-Objective Genetic Algorithm (MOGA). Through a feasibility study involving multiple simulations, including tests under significant external perturbations (wind) and model parameter uncertainty, we validate the proposed upgrades.