Hybrid UAVs have gained a lot of interest for their combined vertical take-off & landing (VTOL) and efficient forward flight capabilities. But their control is facing challenges in over-actuation and conflicting requirements depending on the flight phase which can easily lead to actuator saturation. Incremental Non-linear Control Allocation (INCA) has been proposed to solve the platform’s control allocation problem in the case of saturation or over-actuation by minimizing a set of objective functions. This work demonstrates INCA on quadplanes, an in-plane combination between a quadrotor and a conventional fixed-wing, and proposes an extension to control the outer loop. The novel controller is called Extended INCA (XINCA) and adds the wing orientation as a force-generating actuator in the outerloop control optimization. This leads to a single controller for all flight phases that avoids placing the wing at negative angles of attack and minimizes the load on hover motors. XINCA has low dependence on accurate vehicle models and requires only several optimization parameters. Flight simulations and experimental flights are performed to demonstrate the performance.

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Controlling over-actuated Unmanned Aerial Vehicle (UAV) is an important task to achieve reliable fail-safe autonomous flight. Incremental Non-linear Control Allocation or INCA has been proposed to solve the platform’s control allocation problem by minimizing a set of objective functions with a method known as the Active Set Method. This work proposes an extension to INCA to control the outer loop of a quadplane UAV, an in-plane combination between a quadrotor and a conventional fixed-wing. The novel controller is called Extended INCA or XINCA and optimizes a mix of physical actuator commands and angular control setpoints fed to the vehicle’s inner loop. It does so while adapting to varying flight phases, conditions, and vehicle states, and taking into account the aerodynamic properties of the main wing. XINCA has low dependence on accurate vehicle models and requires only several optimization parameters. Flight simulations and experimental flights are performed to prove the performance of both controllers. @en