A Non Linear Model Predictive Controller (NMPC) for the realization of an autonomous take-off procedure of a rigid wing Airborne Wind Energy (AWE) system is proposed. The addressed take-off approach employs a linear motion system to accelerate the aircraft to take-off speed, a winch which is connected to the aircraft with a tether, and finally on-board propellers to drive the aircraft to operational altitude. A comparison between the performance of the NMPC with the Linear Quadratic Regulator (LQR) is made to determine whether it is necessary to use advanced non-linear controllers in the considered takeoff procedure. Simulations results demonstrate that the NMPC outperforms the LQR in terms of pitch angle and velocity tracking as well as time to reach a desired minimum altitude. Additionally, a comparative power analysis was conducted and results indicate that the NMPC controller is more energy efficient since it is able to drive the aircraft to a larger altitude with smaller power losses.

Advanced Air Mobility aircraft designs following the Simplified Vehicle Operations (SVO) concept require novel environments for practical and intuitive pilot training. Mixed Reality (MR) technologies can support immersive and interactive learning methods for operating several SVO aircraft, including electric Vertical Take-Off and Landing (eVTOL) systems. Despite this potential, regulatory guidelines for simulator-based eVTOL pilot training, especially concerning the Instructor Operator Station (IOS) design, are nascent and require substantive development. This paper investigates the feasibility of an MR eVTOL research simulator as a training tool for instructors. A user study forms the basis for a bottom-up categorization of the instructor’s performance shaping factors, which are pivotal for the design of an MR IOS. This paper contributes to the discourse on MR integration in pilot training by identifying key enhancements necessary for an IOS design.

This paper presents a modular flight guidance and control architecture existing at the Institute of Flight System Dynamics at TUM, which is utilized for various flight applications. The focus is laid onto the novel tracking command and feedforward rates interface connecting the kinematic outer loops and the inner loop controller, which uses the aircraft dynamics by means of aerodynamic effectors. Simulation examples illustrate the trajectory tracking performance and the effects of the command interface.

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

In this paper, novel control and update laws for predictor-based Model Reference Adaptive Control for the case of over-actuated systems are presented. A weaker assumption than positive definiteness of the control effectiveness matrix is formulated. This allows to take actuator failures and even changes in the control direction into account. Furthermore, it is shown that the number of adaptive parameters can be reduced avoiding over-parameterization. The control law is applied to a hexarotor example and simulation results demonstrate the control law's capabilities.

Traditional autopilots suffer from inherent flight path control objective conflict problems, as arbitrary flight path and speed targets cannot be maintained with saturated energy rate control. Elementary flight envelope protections are typically introduced to protect airspeed and prevent loss of control, but rather as a "last line of defense", than as a mean of smooth and deterministic control objective resolution during normal operation. In this paper, an approach for active energy distribution prioritization and integrity protection, as integrated part of the flight path controller of a modular flight guidance and control system, is presented. The approach allows speed or flight path angle maneuvering to be prioritized in case of saturated energy control, with automatic speed priority at the edges of the envelope in order to ensure the airspeed integrity of the aircraft. The approach is analyzed and validated using high-fidelity simulations of the full closed loop system for various conditions.

An automatic flight path controller, as part of a modular automatic flight guidance and control system, is presented, along with initial flight test results using a DA42 M-NG flying testbed. The basic principle for the flight path control is a reference model based dynamic inversion of the kinematic equations of motions, with pseudo-control hedging to account for inner loop dynamics and plant response deficits. The kinematic frame flight path commands are transformed into body-frame commands executed by inner loop and autothrust controllers for transverse and linear force control. Initial flight test results are presented, demonstrating the feasibility of the path control approach, with good tracking and disturbance performance already during early flight testing.

The SAGITTA Research Demonstrator is a flying-wing UAV testbed. Its digital flight control system, which is being developed by the Institute of Flight System Dynamics of the Technical University Munich, consists of cascaded control loops. In a UAV operation scenario, different control loops need to be engaged at different times or connected in different ways to fulfill a given mission. This has to be done with respect to commands from the flight operator or even automatically based on sensor information or data link availability. The task is handled by a system automation module which is part of the flight control system software. Its structure and implementation are introduced and described in this paper.

The SAGITTA Demonstrator is a novel UAV with a digital flight control system being developed by the Institute of Flight System Dynamics of the Technical University of Munich. This paper presents the implementation of an automatic take-off algorithm as a system automation module of the auto flight system. Based on a phase-breakdown of take-off, the implementation features a state machine that covers the procedure of the maneuver and the transition conditions from one phase to another. Through this state machine, the automatic take-off algorithm enables controller modules of the auto flight system and provides corresponding commands for the conduct of take-off. The design of the automatic take-off algorithm focuses on maximizing safety and robustness against uncertainties and disturbances. Presented simulation results prove the concept of the algorithm as well as its robustness to simulated reality effects.

Model Reference Adaptive Control facilitates nonlinear systems to adapt to modeling errors, environmental changes or structural damage. Most adaptive control frameworks employ Lyapunov analysis in order to establish stability of the closed loop system. However, the derived signal bounds are inherently conservative. Furthermore, these bounds are seldom known; the plant states might still violate structural requirements, thus causing the system to fail. In order to counter this disadvantage, we employ an adaptive output consistency monitor, which is based on Bayesian linear regression. It learns a model of the adaptive controller from online gathered data and thus allows for the assertion whether the adaptive output is within an interval of the expected output. The method is demonstrated in flight tests of a quadcopter with and without robustness modification.

The recent emergence of unmanned aerial vehicles asked for both well-performing auto-flight systems and fly-by-wire architectures. Towards this end, the trajectory control module of an integrated auto-flight control system is introduced in this paper, which utilizes nonlinear second-order error dynamics of the position error and uses nonlinear dynamic inversion as a control methodology. Flight test results of a flightplan mission conducted on the institute's general aviation aircraft - a DA42 augmented with experimental fly-by-wire - are presented and the controller's performance is evaluated.

Although adaptive control offers a unique way to control dynamical systems without excessive reliance on their mathematical models, it is well known that nonidentical command profiles result in nonidentical closed-loop responses with such controllers, and therefore, it is of practical interest to study theoretical ways to construct predictable closed-loop responses when adaptive controllers are in the loop. To that end, we introduce a new concept called scalability to adaptive control in this paper. In particular, we analyze how to scale learning rates of adaptive weight update laws of various adaptive control schemes with respect to given command profiles to achieve a predictable closed-loop response. Illustrative numerical examples are provided to demonstrate the proposed concept and a possible utilization for the evaluation of adaptive controllers.

In this paper, a trajectory generation algorithm within an integrated flight guidance and control system is presented. The approach uses clothoids to deal with the problem of curvature steps during the transition phase between straight line and arc flight. The algorithm is designed in interaction with a trajectory controller for 2ndorder error dynamics, which hence requires more trajectory information than a conventional approach. The presented test results were obtained via in-flight tests with an experimental CS 23 aircraft.

In this paper, we present a system for controlling a quadrocopter using both optical and inertial measurements. We show how to use external stereo camera measurements for visual servoing, by onboard fusion at high rates, only natural features provided by the vehicle and without any active marker. In our experiments, we show the accuracy and robustness of our system during indoor flights, as well as robustness to external flight disturbances.