TY - JOUR
T1 - Fault-Tolerant Control for Path-Following of Independently Actuated Autonomous Vehicles Using Tube-Based Model Predictive Control
AU - Wu, Xitao
AU - Wei, Chao
AU - Tian, Hanqing
AU - Wang, Weida
AU - Jiang, Chaoyang
N1 - Publisher Copyright:
© 2000-2011 IEEE.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - This paper designs a fault-tolerant controller for the path-following of independently actuated (IA) electric autonomous vehicles (AVs) with steer-by-wire (SBW) systems. Such a controller can handle the effect of steering motor fault and the bounded disturbances of the vehicle dynamic system. Firstly, we formulate the dynamic models of the vehicle, the tire, the SBW system, and the steering motor. Secondly, with the models, we identify the effectiveness coefficient of the steering motor and calculate the steering resistance torque. Both the two parameters are used to determine the potential maximum front-wheel angle. The effectiveness coefficient can determine when the control system switches to the fault-tolerant control (FTC) mode. For the FTC mode, we then introduce a tube-based model predictive control (MPC) framework to guarantee vehicle stability in steering processes and maintain the tracking performance. The disturbances of the vehicle dynamic system affect both the control input and the state. On one hand, the disturbances are formulated to be a tightened state constraint. On the other hand, together with the potential maximum front-wheel angle, the disturbances are formulated to be a tightened input constraint. Finally, we show the effectiveness of the designed fault-tolerant controller via Carsim-Simulink joint simulation and real-vehicle experiment.
AB - This paper designs a fault-tolerant controller for the path-following of independently actuated (IA) electric autonomous vehicles (AVs) with steer-by-wire (SBW) systems. Such a controller can handle the effect of steering motor fault and the bounded disturbances of the vehicle dynamic system. Firstly, we formulate the dynamic models of the vehicle, the tire, the SBW system, and the steering motor. Secondly, with the models, we identify the effectiveness coefficient of the steering motor and calculate the steering resistance torque. Both the two parameters are used to determine the potential maximum front-wheel angle. The effectiveness coefficient can determine when the control system switches to the fault-tolerant control (FTC) mode. For the FTC mode, we then introduce a tube-based model predictive control (MPC) framework to guarantee vehicle stability in steering processes and maintain the tracking performance. The disturbances of the vehicle dynamic system affect both the control input and the state. On one hand, the disturbances are formulated to be a tightened state constraint. On the other hand, together with the potential maximum front-wheel angle, the disturbances are formulated to be a tightened input constraint. Finally, we show the effectiveness of the designed fault-tolerant controller via Carsim-Simulink joint simulation and real-vehicle experiment.
KW - Autonomous vehicles (AVs)
KW - fault-tolerant control (FTC)
KW - steer-by-wire (SBW) system
KW - tube-based model predictive control (MPC)
UR - http://www.scopus.com/inward/record.url?scp=85135748213&partnerID=8YFLogxK
U2 - 10.1109/TITS.2022.3191755
DO - 10.1109/TITS.2022.3191755
M3 - Article
AN - SCOPUS:85135748213
SN - 1524-9050
VL - 23
SP - 20282
EP - 20297
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 11
ER -