Power output during flight operation of multi-megawatt airborne wind energy systems is substantially affected by the mass of the airborne subsystem, resulting in power fluctuations. In this paper, an approach to control the tether force using the airborne subsystem is presented that improves the quality of the power output. This kite tether force control concept is implemented on the 3DOF dynamic simulation of the MegAWES reference model. First, the winch of MegAWES is resized because an analysis of winch inertia and radius shows its effect on power output and tether force overshoot. Second, the power consuming sections during the traction phase are eliminated by using a feedforward winch controller. Finally, the peak power is substantially reduced by implementing the kite tether force controller which uses a measurement of the tether force, angle of attack, and airspeed to keep the tether force constant when the system is at its power limit. This reduces the range between minimum and maximum power output by 75%.

Incremental Nonlinear Dynamics Inversion (INDI) flight controllers are sensor-based control systems, that are robust towards model uncertainty and with good disturbance rejection characteristics. These controllers show coupling effects in structural modes when implemented in specific flying vehicles with low-frequency structural motions. This paper investigates different INDI implementations, standard INDI, hybrid INDI, and notch filter placement in the INDI loop via simulation and flight tests on the Nederdrone. System identification of the structural characteristics of the vehicle and the system’s yaw dynamics are executed via ground vibration and hover flight tests. Closed-loop behaviour of theINDI inner-loop, disturbance rejection performance, and outer loop step-tracking performance was assessed with dedicated flight tests. The investigated INDI solutions show similar disturbance rejection and outer-loop step tracking performance, while the hybrid INDI solution performs a better nonlinear dynamic inversion. Index Terms—INDI, complementary filter, unmanned vehicle, flight control system structural motion coupling

Incremental Nonlinear Dynamic Inversion (INDI) is a sensor-based control strategy, which has shown robustness against model uncertainties on various aerospace vehicles. The sensor-based nature of the method brings attractive properties which has made it popular in the last decade. INDI globally linearizes the system by making use of control input and state derivative feedback. The time lag between control input and state derivative is seen as one of the main challenges of the method. This has been partially solved by synchronization of actuator outputs with measurement, although the method is still vulnerable to unexpected measurement delays. This paper proposes Hybrid INDI approach to alleviate measurement delays and to reduce sensor dependence of INDI. The approach fuses the system model and sensor measurement via a complementary filter that produces angular acceleration estimation. The estimation responds fast to the system input thanks to the on-board model. It has also a low-frequency accuracy because of the sensor measurement. The method is found to retain good performance in case of model mismatches and measurement delays. An attitude controller is designed for the F-16 aircraft model and findings are verified with time-domain simulations.

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 𝐶* 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.

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.