Quad-planes combine hovering and vertical takeoff and landing capability with fast and efficient forward flight. Regular Quad-planes with dedicated pusher motor can be subject to gust disturbances, and are not well-equipped to deal with actuator faults. Dual-axis Tilt-Rotor quad-planes are more maneuverable due to their overactuation. This also increases their gust resilience and allows them to hover statically after actuator failures. The vehicle in this paper uses an Incremental Nonlinear Dynamic Inversion (INDI ) controller, combined with a nonlinear Sequential Quadratic Programming (SQP) Control Allocation (CA ) algorithm, which can also find hover solutions in the case of actuator failures. We investigate both a combined allocation of linear and angular accelerations, as well as a cascaded allocation scheme. Due to large required changes in roll and pitch angles, the cascaded approach is selected in this research. Introduction of a tertiary control effort term, separation of attitude and actuator command optimization and a simulated Fault Detection and Identification ( FDI) mechanism led to repeated successful recovery from a motor failure in hover. Position tracking was demonstrated under failure in the recon- figured flight condition. Index Terms- Tilt-rotor, dual-axis tilt, quad-plane, FTC, over- actuated, control allocation

Quad-planes are a type of vehicle which combine the hovering capability of quadcopters and the forward flight efficiency of winged aircraft. Flight tests conducted on a dual-axis tilting-rotor quad-plane, designed to fly without aerodynamic surfaces, observed that the quad-plane suffered from insufficient roll authority during fast, forward flight. Subsequent wind tunnel testing confirmed a two- to fourfold reduction in roll moment generation from propellers mounted in front of the wing at similar levels of tilt as their rear counterparts, caused by propeller-wing interactions. To address the mismatch in actuator effectiveness shown by the wind tunnel experiment, the effect of the propeller-wing interactions was incorporated into the aero-propulsive model by means of a global polynomial, the structure of which was found using multivariate orthogonal function modelling. New flight tests demonstrated that, by including the propeller-wing interactions in the control allocation, the vehicle is capable of tracking a figure 8 maneuver without aerodynamic surfaces, and without compromising tracking performance.

The increasing use of drones for both civilian and military applications has raised substantial safety and security concerns, including risks of misuse in restricted airspaces and on the battlefield. Loitering kamikaze drones, in particular, repre- sent a significant threat, necessitating effective countermeasures. This paper proposes an onboard interception guidance algorithm designed to detect, track, and intercept such drones, relying on data from a camera and simulated radar input. The system is initiated using the target’s initial location provided by ground- based sensors, while the onboard system processes camera data and simulated radar-derived distance values. The key challenge is ensuring successful interception within the limited operational range of these sensors. Several detection algorithms are im- plemented and compared, and the chosen detection method is integrated with the OpenCV CSRT tracker. A linear Kalman filter is employed to estimate the target’s position and velocity, effectively handling intermittent missed detections. The proposed solution is evaluated by intercepting a simulated target UAV with a real interceptor drone in TU Delft Cyberzoo, feeding targets position and velocity to the interceptor, alongside validating the detection and tracking algorithms.

Fixed-wing aircraft fly longer, faster, and further than rotorcraft, but cannot take off or land vertically. Hybrid drones combine VTOL with a wing for forward flight, but the hovering system generally makes them less efficient than a pure fixed-wing. We propose an alternative, in which a rotorcraft is used to assist the fixed-wing UAV with the VTOL portions of the flight. This paper takes the first steps towards this alternative by developing and testing an overactuated rotorcraft that can autonomously dock onto a target at fixed-wing velocities. The control system uses Incremental Non-Linear Dynamic Inversion Control (INDI) to achieve linear accelerations with lateral and longitudinal motors, enabling robust horizontal control independent of attitude. A relative guidance algorithm for the docking approach path is presented, along with a vision sensing approach using ArUco markers and IR LEDs. Successful docking and separation were achieved in the wind tunnel at speeds of up to $15$m/s.

Pitot tube-free airspeed estimation methods exist for fixed-wing and multirotor configurations, but lack direct applicability to hybrid unmanned air vehicles due to their wide flight envelope and changing dynamics during transition. This work proposes a novel synthetic air data system for the Variable Skew Quad Plane (VSQP) hybrid vehicle to allow airspeed estimation from hover to high speed forward flight and provide pitot tube fault detection. An Extended Kalman Filter fuses Global Navigation Satellite System (GNSS) and inertial measurements using model-independent kinematics equations to estimate wind and airspeed without the use of the pitot tube. The filter is augmented by a simplified vehicle force model. Pitot tube fault detection is achieved with a simple thresholding operation on the pitot tube measurement and the airspeed estimation residual. Accurate airspeed estimation was validated with logged test flight data, achieving an overall 1.62 m/s root mean square error. Using the airspeed estimation, quick detection (0.16 s) of a real-life abrupt pitot tube fault was demonstrated. This new airspeed estimation method provides an innovative approach for increasing the fault tolerance of the VSQP and similar quad-plane vehicles.

Unmanned Aerial Vehicles (UAVs) are increasingly being used in various applications, which demand longer endurance, extended range, and high maneuverability. These requirements necessitate the development of effective control methods for Hybrid UAVs. In this paper, we propose an outer loop Incremental Nonlinear Dynamic Inversion (INDI) controller for Hybrid UAVs, based on an analytically derived control effectiveness to control the linear acceleration of the UAV. The control effectiveness is derived in a new frame that does not show singularities, technically allowing controlled flight at all attitudes. For trajectory tracking purposes, a Proportional Derivative (PD) controller is added. In simulation the proposed controller shows comparable results to already existing INDI controllers for hover and forward flight. When performing loop the loops it is shown that the proposed control system is able to handle high roll angles, while the already existing INDI controller crashed.

An effective distribution of flight control commands over many aircraft actuators (engines, control surfaces, flaps, etc.) can be achieved with constrained optimisation. Active-Set methods solve these problems efficiently, but their computational time requirements are still prohibitive for aircraft with many actuators or slower digital flight control processors. This work shows how these methods can be improved in these regards, by updating the required matrix factorisations at lower computational costs, rather than solving a separate optimisation problem at every step of the iterative algorithm. Additionally, it is shown how the sparsity of the problem matrices can be exploited. Both open-loop simulations and flight tests have been performed, which show that worst-case timings for a 6-rotor multicopter UAV can be improved by 65% over a current Active-Set solver. Furthermore, methods are presented that remedy numerical stability issues occurring in micro-controller floating point arithmetic but introduce a small but measurable adverse effect on the flight behaviour.

Capable of both vertical take-off and landing and forward flight, tail-sitters are a versatile class of UAVs with a large range of potential applications. A variant of tailsitters using tilt-rotors instead of ailerons for pitch and roll control has been proposed to mitigate the reduced control authority at low to zero velocities. The control of the translational dynamics for this platform is uniquely challenging. The extended flight envelope requires the controller to be able to perform hover and forward flight which are two flight phases with very different dynamics. Additionally, the tilt-rotor mechanism used to control the system is highly nonlinear which adds to the challenge. This paper presents a novel acceleration controller using Nonlinear Model Predictive Control (NMPC) in addition to the use of behavioural cloning to mimic the NMPC using a feedforward neural network. It is shown that behavioural cloning does successfully transfer general flight characteristics but that the performance is degraded with respect to the NMPC. Additionally, a sensitivity analysis was performed to investigate the effects of improper parameter estimation on controller performance. The most interesting result from this analysis is the strong sensitivity of both controllers to changes in centre of gravity location and mass.

The transportation of payloads utilizing multiple drones presents a promising application for lifting heavier loads that exceed the payload capacity of a single drone. However, the cable-suspended payload introduces significant challenges to the system, and this research area remains relatively unexplored. In this work, a novel solution for payload-carrying application is proposed. First, the dynamics of cable-suspended payload transportation using multiple quadrotors, taking into account the influence of drag forces on the quadrotors are studied. A nonlinear optimization is employed to control the payload while distributing the control effort required for manipulating the suspended load over the drones in the formation while ensuring both tension constraints and collision avoidance between drones in the formation. The feasible path commands for formation agents are computed from the optimization. One of the critical aspects for controlling such a system is the load-introduced force, which exhibits rapid and complex variations. To address this, an extended state observer is employed to estimate the load force, eliminating the need for a tension sensor. In pursuit of a robust framework, a formation reset strategy is also developed, allowing to maintain load tracking performance and ensure the safety of formation agents, even in the event of a malfunction in one of the drones. A series of simulations are conducted to validate the effectiveness and robustness against disturbance and suspension failure of the proposed strategy and controllers. Results demonstrate that the whole multi-lift system can handle external disturbances, model uncertainties regarding drone inertia, mass and load mass, as well as suspension failures.

Recent literature in real-time trajectory planning has proposed using Control Barrier Functions (CBFs) as collision constraints in Model Predictive Control (MPC) for efficient guidance, a concept referred to as MPC-CBF. This concept has been explored for both first and second-order CBFs. However, these approaches relied on an analytical description of the environment. Building upon this, we propose combining MPC-CBF with Euclidean Signed Distance Fields (ESDFs), eliminating the need for such an analytical model of the environment. Notably, we extend this approach to a new field by applying it to Unmanned Aerial Vehicles (UAVs). Through simulations, we compare flown trajectories and noise robustness for distance constraints, first-order CBF constraints and second-order CBF constraints. First-order CBF constraints outperform distance constraints, excelling in path planning and noise resilience. Second-order CBF constraints face challenges due to numerical approximations of the hessian of the ESDF and stricter dependency on an accurate acceleration model, limiting their practicality for UAVs. The proposed control framework was tested by safely maneuvering an enterprise inspection drone around a Boeing 787-9 aircraft inside an aircraft hangar, confirming its effectiveness in collision avoidance and real-world scenarios.

This project proposes and evaluates a novel concept for an airspeed instrument aimed at small hybrid unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer formed over the vehicle’s body to its airspeed. The instrument consists of two microphones, flush mounted on the UAV’s nose cone, that capture the pseudo-sound caused by the coherent turbulent structures, and a micro-controller that processes the signals from the microphones and computes the airspeed. Dedicated models were constructed, using data obtained from wind tunnel and flight experiments, that take the power spectra of the microphones’ signals as an input and provide the airspeed as an output. The model structure is a feed-forward neural network with a single hidden layer, trained using a second-order gradient descent algorithm, following a supervised learning approach. The models were validated using only flight data, with the best one achieving a mean approximation error of 0.043 m/s and having a standard deviation of 1.039 m/s. It was also shown that the airspeed could be successfully predicted for a wide range of angles of attack, given that they are known, thus necessitating the vehicle to be equipped with a dedicated angle of attack sensor.

This paper presents the design of an Incremental Nonlinear Dynamic Inversion (INDI) controller for the novel platform VSQP. Part of the identified challenges is the develop- ment of a model for the actuator effectiveness and lift especially as a function of skew, the newly added degree of freedom. In particular it is assumed that the actuator effectiveness changes linearly with actuator state and that aerodynamic forces change quadratically with airspeed and depend mainly on the chordwise component of airspeed. Moreover, the position of the moving actuators is expressed as a function of the corresponding moment arm and the skew angle. The models and assumptions are verified through static and dynamic wind tunnel tests at the OJF of TuDelft. A WLS routine is used to solve the control allocation for the overactuated guidance loop. A lower cost is assigned to the use of the push motor so to steer the control allocation in its favor rather than commanding changes in attitude. A gradual switch of the hover motors in transition is achieved by scheduling the lift-pitch effectiveness with airspeed. Therefore, as airspeed increases the outerloop INDI controller evaluates that changing pitch to achieve a certain vertical acceleration set point results in an increasingly cheaper command allocation than changing thrust. An automatic skew controller is designed based on the developed control moment and lift models. The skew angle is scheduled with airspeed so to perform transition while also maximizing control authority. Finally, the controller is validated by performing multiple transitions inside the OJF windtunnel.

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

In recent years the popularity of VTOL (Vertical Take-Off and Landing) drones has increased significantly. Due to their hybrid design, these drones can take off and land vertically and fly horizontally, enabling them to land in difficult terrain and have a more extensive range than the Quadcopter counterpart. However, this hybrid design also introduces complex dynamics that are difficult to model. For adequate control, this requires an adaptive element that can compensate for the modeling errors. Due to the significant change in flight conditions, adaptations must be made effectively over the entire flight envelope of a VTOL drone. This thesis introduces an adaptive controller that can cope with the large flight envelope and varying flight conditions of the VTOL drone and can adapt the controller effectively and store previous adaptations with multivariate B-splines during real-time flights.

This paper proposes a control strategy based on incremental nonlinear dynamic inversion (INDI), meant for trajectory tracking purposes. The controller extends the conven- tional capabilities of INDI by including actuator dynamics in the inversion law and introducing a state dependent compensation term to reduce the effort of the error controller. A complementary filter is employed to reduce the degrading effect introduced by the filtering-induced delay in the feedback loop. Both simulated and real flight tests are conducted on a quadrotor configuration with artificially slowed down actuators and a drag plate mounted on top, to better observe the effect of actuator dynamics and state dependent dynamics in trajectory tracking accuracy. Simulations show that the combination of the two additional features increases tracking accuracy both in the short and long term response. It is also found that an overestimation of the state compensation term leads to instability, which makes the strategy not robust to model mismatch. Real flight tests, involving the tracking of a series of doublets on the pitch attitude and a lemniscate of Bernoulli, show that, as the complexity of the maneuver increases, the less the state compensation term effectively contributes to an improved tracking when the model is incomplete. On the other hand, trajectory tracking accuracy due to the consideration of actuator dynamics shows consistency and improvement respect to conventional INDI solutions.

Drones have been an emerging trend in the last few years. They are used in multiple industries already, from photography and videography to racing. More use cases are now being conceived, such as using drones to deliver packages and food to people at home, using drones for inspections, or even using them as rescue searching vehicles in hostile environments. Even more possibilities open up once the drones bundle their forces to create swarms. The lifting capabilities of drones are still somewhat limited, but in a swarm they might be able to lift heavy payloads. This report covers the design of a concept of a payload carrying swarm, intended to lift cargo up to 500 kg to even the top of a tall building.

Unmanned Aerial Vehicles, UAVs, serve many purposes
these days, such as short-range inspections
and long-distance search and rescue missions. Long-distance missions can entail a search in a building. Such missions require a large aircraft for endurance and a small aircraft for manoeuvrability in a building.

This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air disassembly followed by separate flight. During joint flight, the quadcopter and the biplane have no intercommunication.

This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight
release is successfully tested in joint hover up to a forward pitch angle of -18 [deg]. Secondly, three control strategies for the quadrotor are compared:
a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.
In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies
are of minimal influence. However, the angular rate damper
strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.

By combining the ability to hover with a wing for fast and efficient horizontal flight, hybrid unmanned aircraft extend the flight envelope and therefore mission capabilities of unmanned aircraft. However, this comes at a cost: increased complexity control-wise and being more susceptible to wind disturbances. This susceptibility to wind gusts is particularly problematic for tailsitters as during hovering and vertical flight their wing is perpendicular to horizontal wind disturbances, often leading to actuator saturation. This paper presents a novel tailsitter micro air vehicle with two leading edge tilting rotors serving as its only actuators. It is shown that thrust vectoring generates sufficient control moment generation alleviating actuator saturation. Incremental nonlinear dynamic inversion (INDI) is implemented for attitude control and is demonstrated to compensate for unmodeled forces and moments whilst only relying on actuator control effectiveness and knowledge of actuator dynamics.