Fault Detection and Data-driven Optimal Adaptive Fault-tolerant Control for Autonomous Driving using Learning-based SMPC

Real-time fault detection and active fault-tolerant control on vehicles can prevent potential accidents and ensure safe autonomous driving. Unlike existing methods which are designed for known faults or require perfect knowledge of vehicle models, this paper proposes a novel data-driven optimal adaptive fault-tolerant control method with model learning for automated vehicles to enable effective and safe driving under various faults. The actuator and sensor faults are integrated in one unified model to describe the vehicle dynamic behavior. The vehicle states are estimated online with only limited onboard sensors in vehicles available. An efficient fault detector using a modified sliding innovation sequence method is developed to ensure accurate fault detection. The dynamic model is adapted online using Gaussian Process based on vehicle running data, accurately representing the dynamic driving behavior. A novel learning-based adaptive fault-tolerant control method is designed to optimize the vehicle movement for autonomous path tracking under various faults with stochastic model predictive control. Simulation experiments validate the effectiveness of the proposed method in fault detection, model online learning, and accurate path tracking across various fault scenarios.