Model-based methodology for upgrading UAV systems: Application to the renovation of the propulsion and control systems of a fixed-wing UAV

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.