The Flying-V is a tailless, V-shaped flying-wing type aircraft that promises to offer significant increases in aerodynamic efficiency. Due to its configuration, the Flying-V faces some control and stability related issues. These include limited control authority, pitch break tendencies and non-ideal handling qualities. To enhance the handling qualities of the Flying-V, Incremental Nonlinear Dynamic Inversion (INDI)-based flight control systems have been proposed. INDI, a sensor-based alternative to conventional Nonlinear Dynamic Inversion (NDI), is rooted in the principle of feedback linearization. Unlike NDI, INDI does not depend heavily on accurate on-board models (OBM), thereby offering increased robustness to aerodynamic uncertainties. However, singular perturbations—such as time delays, aeroelastic effects, and additional unmodeled or unknown dynamics—have been identified as challenges for INDI-based control laws. Various strategies have been explored to improve the overall robustness of INDI-based flight control systems, including outer-loop tuning and inversion loop augmentation strategies. In this research a multi-loop µ-optimal approach for designing robust inversion-based flight control laws is explored for the design of an explicit model-following pitch-rate control system for a short-period approximation of the Flying-V’s longitudinal dynamics. The design problem takes into account both regular and singular perturbations. To assess the robust stability and performance of the proposed control systems, a structured singular value analysis was performed. It was concluded that a multi-loop synthesis approach is capable of achieving better robust stability and performance levels when compared to either strictly inner-loop or outer-loop synthesis. As such, it can be concluded that multi-loop synthesis approaches are best capable of leveraging the robustness functionalities of multi-loop inversion-based control systems.

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

Control augmentation systems based on Incremental Nonlinear Dynamic Inversion (INDI) are able to provide high-performance nonlinear control without the need for a model of the complete system. Considering a pitch rate control law for a fixed-wing aircraft, only a model for the elevator control effectiveness (CE) and sensor feedback of the pitch acceleration are required for the model inversion. Despite the increased robustness against model error due to the decreased model dependency, control laws based on INDI could still show performance variation when the on-board CE model deviates from the actual CE. This thesis will put this in the context of the control performance of high-performance aircraft. Furthermore, multiple adaptive INDI control techniques are investigated as possible solutions. This study revealed that adaptive control based on Least-Mean-Square (LMS) parameter estimation has the most potential. A second analysis using handling quality and stability (HQ&S) requirements showed that this approach is able to decrease variation in the HQ&S as well. However, the results also show that HQ&S variation for large centre of gravity shifts could not always be fully mitigated by adaptive control as it is possible that the time-scale separation assumption becomes impaired.

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.