Quad-planes combine hovering and Vertical Takeoff and Landing (VTOL) capabilities with efficient forward flight. However, they are often vulnerable to gust disturbances and are not well-equipped to handle actuator faults. Dual-axis Tilt-Rotor quad-planes offer enhanced maneuverability due to their overactuation, which also enables stable hovering even after actuator failures. These vehicles can employ an Incremental Nonlinear Dynamic Inversion (INDI ) controller paired with a nonlinear Sequential Quadratic Programming (SQP ) Control Allocation (CA ) algorithm that can find hover solutions under actuator failure conditions. We explore both a combined allocation of linear and angular accelerations and a cascaded allocation scheme. Due to the large required changes in roll and pitch angles, the cascaded approach is selected for this research. The proposed algorithm was tested on a flying vehicle, demonstrating successful hovering and position control capabilities under a simulated Fault Detection and Identification (FDI) mechanism.

Achieving precise and reliable autonomous landings on moving ship decks is a critical challenge for hybrid Unmanned Aerial vehicles (UAVs), particularly under harsh maritime conditions. This dissertation addresses this challenge by developing a nonlinear control framework for a novel dual-axis tilting rotor quad-plane, designed specifically to enhance stability, maneuverability, and precision during landing operations on moving platforms. Operating in such harsh environments requires effective handling of varying wind conditions, ship motion, and rapid position changes.

The dual-axis tilting rotor concept offers unique capabilities that make it particularly suited for precision landing tasks on moving ship decks. Unlike conventional hybrid under-actuated UAV designs, the overactuated nature of the dual-axis tilting rotor quad-plane allows it to maintains full 6 Degrees Of Freedom (DOF) control authority during low airspeed flight. Moreover, its rapid thrust vectoring capability provides exceptional wind rejection performance, enabling precise control even under turbulent maritime conditions. This versatility is essential for maintaining stability and accuracy during complex landing maneuvers where environmental disturbances and ship motion are constantly changing. However, the novel propulsion system also presents significant control challenges due to its non-affine in the input dynamics. Developing advanced control strategies capable of handling these complexities is critical for achieving reliable and precise autonomous landings on moving platforms.

First, a nonlinear programming-based control allocation algorithm is developed to overcome the limitations of state-of-the-art methods that rely on linearized control effectiveness. This assumption may be too restrictive for vehicles with highly nonlinear effector dynamics, such as the dual-axis tilting rotor quad-plane. The proposed control allocation algorithm effectively manages the complex dynamic interactions inherent to the novel propulsion system, enabling smooth and precise control within the low-airspeed flight regime.

Second, the controller is extended to operate across the entire flight envelope, from hovering to forward flight. Unlike traditional hybrid UAV controllers that rely on distinct control strategies for each flight mode, the proposed framework enables seamless transitions by dynamically reallocating control objectives across the available actuators, including adjustments to vehicle attitude as airspeed varies.

Third, this dissertation introduces a real-time actuator state feedback system to enhance the controller’s robustness and adaptability in challenging maritime environments. By eliminating reliance on predefined actuator models, this improvement provides continuous situational awareness of the propulsion system’s health, allowing the controller to dynamically adjust to varying operational conditions. The enhanced hardware architecture enables direct control of motor RPM and angular rotor tilt, significantly improving the system’s resilience against environmental disturbances, actuator degradation, and battery voltage fluctuation. This capability forms the foundation for developing a robust fault-tolerant control framework essential for reliable autonomous landings on moving platforms.

Fourth, leveraging the improved hardware architecture, a fault-tolerant control framework is developed to ensure reliable operation under various actuator failure conditions. The dual-axis tilting rotor quad-plane’s over-actuated design allows the controller to reallocate control efforts dynamically among functional actuators, maintaining stability and control authority even under severe actuator failures. Extensive flight tests validate the fault-tolerant framework’s effectiveness, demonstrating resilience in challenging scenarios.

Finally, a real-time trajectory planning algorithm is developed to enable autonomous landing on moving ship decks. Using a Long Short-Term Memory (LSTM) neural network model, a prediction framework is created to estimate ship motion over a 7-second horizon. This prediction enables the generation of feasible landing trajectories that are continuously reassessed to account for prediction uncertainties and tracking errors. While simulation results demonstrated the approach’s potential, future work will involve testing the algorithm in real-world conditions.

The proposed control framework, combined with the novel dual-axis tilting rotor quad-plane design, offers a robust and adaptable solution for achieving reliable autonomous landings on moving platforms. The methodologies developed in this thesis can be further extended to various hybrid UAV configurations, providing valuable insights for broader applications in complex operational environments.

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. It was hypothesized that the propellers located in front of the wing are less efficient in generating a rolling moment due to potential propeller-wing interactions. Wind tunnel tests, performed at TU Delft's Open Jet Facility, 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. 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 of the quad-plane by means of a global polynomial, the structure of which was found using multivariate orthogonal function modelling. This augmented aero-propulsive model was then integrated into the sequential quadratic programming based control allocation algorithm used by the quad-plane. 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.

Hybrid Unmanned Aerial Vehicles UAV are vehicles capable of take-off and landing vertically like helicopters while maintaining the long-range efficiency of fixed-wing aircraft. Unfortunately, due to their wing area, these vehicles are sensitive to wind gusts when hovering. One way to increase the hovering wind-rejection capabilities of hybrid UAV is through the addition of extra actuators capable of directing the thrust of the rotors. Nevertheless, the ability to control UAVs with many actuators is strictly related to how well the Control Allocation problem is solved. Generally, to reduce the problem complexity, conventional (CA) methods make use of linearized control effectiveness in order to optimize the inputs that achieve a certain control objective. We show that this simplification can lead to oscillations if it is applied to thrust vectoring vehicles, with pronounced non-linear actuator effectiveness. When large control objectives are requested or actuators saturate, the linearized effectiveness based CA methods tend to compute a solution far away from the initial actuator state, invalidating the linearization. A potential solution could be to impose limits on the solution domain of the linearized CA algorithm. However, this solution only reduces the oscillations at the expense of a lag in the vehicle acceleration response. To overcome this limitation, we present a fully nonlinear CA method, which uses an Sequential Quadratic Programming (SQP) algorithm to solve the CA problem. The method is tested and implemented on a single board computer that computes the actuator solution in real time onboard a dual axis tilting rotor quad-plane. Flight test experiments confirm the problem of severe oscillations in the linearized effectiveness CA algorithms and show how the only algorithm able to optimally solve the CA problem is the presented Nonlinear method.

This paper presents a modular autopilot framework for Unmanned Aerial Vehicles (UAVs) that addresses the limitations of modern flight controllers. The framework utilizes separate and external subsystems for actuator control and the resolution of the Control Allocation problem. The actuator subsystem, implemented on a Teensy 4.0 microcontroller, incorporates an Incremental Dynamic Inversion Control (INDI) RPM controller, enabling direct control of motor RPM and facilitating the implementation of dynamic inversion-based control laws. The primary flight computer, a Cube Orange, coordinates the system, while an OrangePi 5 singleboard computer serves as a companion computer. Real flight results demonstrate the effectiveness of the framework, highlighting its potential for robust and efficient UAV control.

In the last few decades, the UAV research has been focusing on hybrid vehicles with Vertical Takeoff and Landing (VTOL) capabilities. Opposed to copters, hybrid vehicles are highly influenced by wind disturbances. This paper presents a novel quad-plane design that uses four dual-axis tilting rotors to enhance the wind rejection capability of a conventional quad-plane vehicle. After the non-linear mathematical model derivation and the actuator identification, the performance of the vehicle is addressed and compared to a conventional quad-plane in simulation, showing a factor 3.4 improvement in linear acceleration reaction time and a reduction of the gust induced displacement of 80%. Free-flight wind tunnel experiments confirmed the simulation outcome and extended the vehicle wind rejection capabilities behavior also to the lateral gust scenario.