@inproceedings{4c36c7eebde041d1a6ce6b4fad8c19fa,
title = "Adaptive Fault Tolerant Control for Safe Autonomous Driving using Learning-based Model Predictive Control",
abstract = "This paper presents an adaptive fault tolerant control approach for autonomous vehicles (AV) under actuator or sensor faults to improve driving safety. A learning-based stochastic model predictive control (SMPC) strategy incorporating vehicle real dynamics characteristics is developed to realize accurate autonomous trajectory tracking. First, a vehicle dynamics model integrating typical actuator and sensor faults is established. Then, a model online learning strategy is designed to update the vehicle dynamics in real-time. Gaussian process (GP) is applied to identify and learn the real dynamic changes caused by faults which is hard to describe by standard models. Finally, the online learning vehicle dynamics is integrated into SMPC to optimize motion control for accurate trajectory tracking. Extensive simulations are studied to evaluate the model online learning performance and the safe tracking performance with adaptive fault tolerant control under various fault conditions.",
keywords = "adaptive fault tolerant control, gaussian process, model learning, optimal trajectory tracking, stochastic model predictive control",
author = "Yu Lu and Yu Yue and Guoqiang Li and Zhenpo Wang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Conference on Mechatronics and Automation, ICMA 2023 ; Conference date: 06-08-2023 Through 09-08-2023",
year = "2023",
doi = "10.1109/ICMA57826.2023.10215568",
language = "English",
series = "2023 IEEE International Conference on Mechatronics and Automation, ICMA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2218--2223",
booktitle = "2023 IEEE International Conference on Mechatronics and Automation, ICMA 2023",
address = "United States",
}