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 paper presents an MPC-based control scheme for unmanned aerial vehicles (UAVs) of quadrotor type that can operate in real-time. The proposed approach is based on a multi-layer model predictive control architecture that uses linear parameter varying (LPV) and feedback linearization techniques in the inner and outer layer, respectively. In the outer layer, the non-linear dynamics of the transitional control are linearized using feedback linearization, followed by the implementation of a combination of linear model predictive control (MPC) and moving horizon estimator (MHE) schemes. In the inner layer, the attitude control utilizes the LPV approach to formulate an LPV MPC controller and MHE estimator. Through simulation using a high-fidelity simulator of a real UAV, the effectiveness of the proposed control scheme is demonstrated in terms of tracking performance and computation time. Furthermore, a comparison with other similar approaches in the literature is included to showcase the advantages of the proposed scheme.