Quad-plane is a popular type of electric vertical and takeoff/landing (eVTOL) vehicle that hybridizes a quadrotor and a fixed-wing airplane. However, the mechanical simplicity of a quad-plane also makes it vulnerable to rotor failures. When a complete rotor fails, it becomes physically impossible to stop the quad-plane from fast yaw spinning, which further induces considerable abnormal aerodynamic forces and moments on the wing. In this paper, a novel incremental adaptive sliding mode control (I-ASMC) is proposed to address these challenges. First, by exploiting sensor measurements, it simultaneously reduces the control model dependency and the minimum possible sliding mode control/observer gains. Second, finite-time convergence is guaranteed in the Lyapunov sense. Third, the control gains are automatically adapted to their minimum possible values without prior-knowledge on the uncertainty bounds. The proposed I-ASMC method is verified on a high fidelity simulation platform with computational fluid dynamic (CFD) aerodynamic models. Simulation results demonstrate that I-ASMC can drive a quad-plane with a complete loss of a single rotor to follow a trajectory. Its robustness to aerodynamic model uncertainties and rotor faults is also better than the linear quadratic regulator (LQR) and the incremental nonlinear dynamic inversion (INDI) control. In conclusion, the reduced model dependency, implementation simplicity, and improved robustness make the proposed I-ASMC promising for enhancing quad-plane safety in real life.

Fault-Tolerant control is crucial for safety-critical systems, such as quadrotors. State-of-Art flight controllers can stabilize and control a quadrotor even when subjected to the complete loss of a rotor. However, these methods rely on external sensors, such as GPS or motion capture systems, for state estimation. To the best of our knowledge, this has not yet been achieved with only onboard sensors. In this letter, we propose the first algorithm that combines fault-Tolerant control and onboard vision-based state estimation to achieve position control of a quadrotor subjected to complete failure of one rotor. Experimental validations show that our approach is able to accurately control the position of a quadrotor during a motor failure scenario, without the aid of any external sensors. The primary challenge to vision-based state estimation stems from the inevitable high-speed yaw rotation (over 20 rd/s) of the damaged quadrotor, causing motion blur to cameras, which is detrimental to visual inertial odometry (VIO). We compare two types of visual inputs to the vision-based state estimation algorithm: standard frames and events. Experimental results show the advantage of using an event camera especially in low light environments due to its inherent high dynamic range and high temporal resolution. We believe that our approach will render autonomous quadrotors safer in both GPS denied or degraded environments. We release both our controller and VIO algorithm open source.

This study has developed a fault-tolerant controller that is able to recover a quadrotor from arbitrary initial orientations and angular velocities, despite the complete failure of a rotor. This cascaded control method includes a position/altitude controller, an almost-global convergence attitude controller, and a control allocation method based on quadratic programming. As a major novelty, a constraint of undesirable angular velocity is derived and fused into the control allocator, which significantly improves the recovery performance. For validation, we have conducted a set of Monte-Carlo simulation to test the reliability of the proposed method of recovering the quadrotor from arbitrary initial attitude/rate conditions. In addition, real-life flight tests have been performed. The results demonstrate that the post-failure quadrotor can recover after being casually tossed into the air.