To reduce the environmental impact of aircraft, technological innovations are required. The Flying-V could be one of these technical innovations, as research shows it could be up to 20 % more efficient than regular aircraft of the same size. The Flying-V however has low lateral control authority, and pitch
break-up could occur for high angles of attack.
The goal of this research is to increase the maturity of the Flight Control System of the Flying-V. To do so, a Flight Control Systems (FCS) is proposed with Flight Envelope Protection, which is based on an Incremental Nonlinear Dynamic Inversion inner loop control law. For longitudinal control, a 6* control law was implemented, and for lateral control, a Rate Control Attitude Hold control law. To test the performance of this controller, the handling qualities of this FCS were determined in the presence of sensor dynamics, as well as discretization effects. The controller is able to be tuned within level 1 handling qualities at selected design conditions, as long as the time delay of the angular rate sensors
is no more than 0.04 seconds.
The effect of sampling time and aerodynamic uncertainties were also evaluated for cruise conditions, and it was shown that the controller is robust for changes in the aerodynamic model, as well as being statically stable for sampling times lower than 0.1 seconds. Finally, the Flight Envelope Protection laws were tested, and shown to meet the requirements, for the cases that were tested, setting a step in maturity for the Flight Control System

Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to promote learning efficiency. On the other, safe learning provides a framework to consider a system’s safety during the learning process. The latter’s contribution is significant on safety-critical systems, such as transport vehicles where stringent (safety) requirements apply. This pa- per proposes a black box safe curriculum learning architecture applicable to systems with unknown dynamics. It only requires knowledge of the state and action spaces’ orders for a given task and system. By adding system identification capabilities to existing safe cur- riculum learning paradigms, the proposed architecture successfully ensures safe learning proceedings of tracking tasks without requiring initial knowledge of internal system dynam- ics. More specifically, a model estimate is generated online to complement safety filters that rely on uncertain models for their safety guarantees. This research explicitly targets linearised systems with decoupled dynamics in the experiments provided in this article as proof of concept. The paradigm is initially verified on a mass-spring-damper system. After that, the architecture is applied to a quadrotor where it is able to successfully track the system’s four degrees of freedom independently, namely attitude angles and altitude. The RL agent is able to safely learn an optimal policy that can track an independent reference on each degree of freedom.