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.

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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. @en

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.

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