Hierarchical adaptive multi-mode fault-tolerant control method for UAVs

Rotor failure induces characteristics in quadrotor UAVs such as strong nonlinearity, rapid time-variance, and severe under-actuation. Consequently, traditional control methods often struggle to maintain flight stability, frequently resulting in loss of control and crashes. To address these challenges, this paper proposes a multi-mode fault-tolerant control method based on a hierarchical adaptive framework. This method innovatively constructs a control architecture comprising Dynamic Weighting Nonlinear Model Predictive Control (DWNMPC) and Adaptive Incremental Nonlinear Dynamic Inversion (AINDI). The upper-level DWNMPC controller employs a state-dependent weight adaptive mechanism. By dynamically adjusting the weights of various states in the cost function based on attitude errors, it prioritizes attitude stability to suppress tumbling at the instant of failure, subsequently transitioning smoothly to precise trajectory tracking once the system stabilizes. To cope with model uncertainties and strong aerodynamic disturbances under severe failure conditions, the lower-level AINDI controller is designed to provide online robust adaptive correction to DWNMPC commands. This controller utilizes sensor measurements to compensate for unmodeled moments in real-time and adopts the Recursive Least Squares (RLS) method with a forgetting factor to identify key parameters, such as the moment of inertia, thereby significantly enhancing system robustness. Experimental results demonstrate that the proposed method exhibits excellent trajectory tracking capabilities under fault-free, partial rotor failure, and complete rotor failure conditions. Furthermore, the control process relies solely on onboard sensors for state estimation, reflecting its high applicability in actual physical environments. In the extreme scenario of complete single-rotor failure resulting in a high-speed spin of approximately −10.5 rad/s, the Root Mean Square Error (RMSE) of trajectory tracking increased by only 0.047 6 m, 0.054 5 m, and 0.083 m on the x, y, and z axes, respectively, compared to the fault-free condition, significantly improving the reliability and safety of the UAV.