Fault-Tolerant Path Tracking Control for Electric Vehicles with Steering Actuator Faults via Learning-Based Fault Detection

• Published in: IEEE International Conference on Industrial Informatics (INDIN) pp. 1 - 6
• Main Authors: Tian, Cheng, Huang, Chao, Huang, Hailong, Zhao, Jing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.08.2024
• Subjects: Actuators; Electric vehicles; Event detection; Fault tolerance; Fault tolerant systems; fault-tolerant control; Motors; Path tracking; Simulation; Steering systems; Torque; Vehicle dynamics; vehicle dynamics and control
• ISSN: 2378-363X
• DOI: 10.1109/INDIN58382.2024.10774426

To enhance path tracking performance in the presence of steering motor faults, this paper introduces an active fault-tolerant control strategy for electric vehicles with four inwheel motors. Firstly, the single-track vehicle dynamics model and steering faults model are established. The control framework includes an upper-level linear parameter-varying model predictive controller for active front steering, an upper-level event-triggered predictive controller for direct yaw control, and a lower-level torque allocation controller. The bi-directional long short-term memory (Bi-LSTM) network is used for low-latency rapid detection of steering system faults. If the fault is detected, the upper-level controller for direct yaw control is triggered to mitigate the negative impact of the steering actuator faults. Based on the high-fidelity CarSim model, the simulation test is conducted under a double-lane change scenario with severe stuck faults in the steering system. The simulation results indicate that the proposed scheme can reduce the cumulative tracking error by 37.86% under the set stuck faults compared with the baseline method.