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The authors have been involved in the development of a fixed wing tailless drone (UAV). We will examine the critical challenge of maintaining longitudinal stability that confronts aircraft with this design [1]-[3]. In the next sections, we will briefly illustrate the type of hardware that was chosen, and, after discussing the aerodynamic challenges, we will address the fuzzy logic resolution method that was used to simplify the calculations. In the current context of flight in which the ground, air and space segments must converse with each other, the ground segment, thanks to Internet-ofThings (IoT) and Wi-Fi technology, is taking on an increasingly important role for UAVs during flight phases and landing via the base station [4]. We can therefore say that Industry 4.0 is making inroads even into the extremely conservative and slow-to-change field of aerospace. This reluctance to change is, of course, due to a key aspect of both air and spaceflight: safety. It took decades of testing and certification to entrust an airliner to the autopilot, and even now, the cabin must be staffed by a human pilot. Contrary to popular belief, safety is not less important in drones, even though there are no people on board. In the case examined in this paper, we entrust the control of longitudinal stability to an automatic system that, far from behaving like a simple machine, uses fuzzy logic to approach human behaviour, while still avoiding human weaknesses [5].

Precise Takagi-Sugeno fuzzy logic system for UAV longitudinal stability: an Industry 4.0 case study for aerospace