This paper proposes a novel fault-tolerant control (FTC) reconfiguration strategy for autonomous vehicles using Model Predictive Control (MPC) based on a Linear Parameter Varying (LPV) model to address steering faults. The proposed approach compensates for the lack of redundant steering actuators by using force differences between the left and right sides of the vehicle to generate corrective yaw moments. By integrating both lateral and longitudinal dynamics, the MPC optimally allocates actuator efforts based on fault severity and desired speed. Simulation results validate the effectiveness of the proposed strategy in maintaining vehicle stability and performance under various fault scenarios, including complete steering failure, thereby ensuring safe autonomous operation.

This study presents an adaptive fault-tolerant approach for the attitude control of a quadrotor unmanned aerial vehicle (UAV) using the backstepping technique and fuzzy logic estimation. The proposed controller addresses sensor and actuator faults with a comprehensive model that includes multiplicative and additive faults. To handle the inherent nonlinearities and disturbances of the system, a modified backstepping controller is employed, combined with an adaptive Fuzzy Logic System (FLS) for estimating unknown nonlinear functions. The adaptive fuzzy fault-tolerant control strategy is formulated to ensure practical fixed-time stability of the closed-loop system even when faults appear. The effectiveness of the proposed approach is validated through simulations, demonstrating its robustness against disturbances and its capability to maintain stable UAV attitude control despite sensor and actuator faults.