TY - JOUR
T1 - Fault-Tolerant Predictive Control with Deep-Reinforcement-Learning-Based Torque Distribution for Four In-Wheel Motor Drive Electric Vehicles
AU - Deng, Huifan
AU - Zhao, Youqun
AU - Nguyen, Anh Tu
AU - Huang, Chao
N1 - Funding information:
This work was supported in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX21_0188, in part by the National Engineering Laboratory of High Mobility Anti-riot Vehicle Technology under Grant B20210017, in part by the National Natural Science Foundation of China under Grant 11672127, and in part by the Fundamental Research Funds for the Central Universities under Grant NP2022408.
Publisher Copyright:
© 1996-2012 IEEE.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - This article proposes a fault-tolerant control (FTC) method for four in-wheel motor drive electric vehicles considering both vehicle stability and motor power consumption. First, a seven-degree-of-freedom vehicle nonlinear model integrating motor faults is built to design a hierarchical FTC control scheme. The control structure is composed of two levels: an upper level nonlinear model-predictive controller and a lower level fault-tolerant coordinated controller. The upper level controller provides an appropriate reference in terms of additional yaw moment and vehicle longitudinal force, required for vehicle stability control, to the lower level controller. This latter aims at distributing the four-wheel torques taking into account both vehicle stability and power consumption. Specifically, the weighting factor involved in the optimization-based design of the lower level controller is determined online by the randomized ensembled double Q-learning reinforcement learning algorithm to achieve an optimal control strategy for the whole vehicle operating range. Moreover, the tradeoff between vehicle stability and power consumption is analyzed, and the necessity of using reinforcement learning is discussed. Numerical experiments are performed under various driving scenarios with a high-fidelity CarSim vehicle model to demonstrate the effectiveness of the proposed control method. Via a comparative study, we highlight the advantages of the new FTC control method over many related existing control results in terms of improving the vehicle stability and driver comfort as well as reducing the power consumption.
AB - This article proposes a fault-tolerant control (FTC) method for four in-wheel motor drive electric vehicles considering both vehicle stability and motor power consumption. First, a seven-degree-of-freedom vehicle nonlinear model integrating motor faults is built to design a hierarchical FTC control scheme. The control structure is composed of two levels: an upper level nonlinear model-predictive controller and a lower level fault-tolerant coordinated controller. The upper level controller provides an appropriate reference in terms of additional yaw moment and vehicle longitudinal force, required for vehicle stability control, to the lower level controller. This latter aims at distributing the four-wheel torques taking into account both vehicle stability and power consumption. Specifically, the weighting factor involved in the optimization-based design of the lower level controller is determined online by the randomized ensembled double Q-learning reinforcement learning algorithm to achieve an optimal control strategy for the whole vehicle operating range. Moreover, the tradeoff between vehicle stability and power consumption is analyzed, and the necessity of using reinforcement learning is discussed. Numerical experiments are performed under various driving scenarios with a high-fidelity CarSim vehicle model to demonstrate the effectiveness of the proposed control method. Via a comparative study, we highlight the advantages of the new FTC control method over many related existing control results in terms of improving the vehicle stability and driver comfort as well as reducing the power consumption.
KW - Electric ground vehicles
KW - fault-tolerant control (FTC)
KW - in-wheel/hub motor
KW - reinforcement learning (RL)
KW - torque vectoring
KW - vehicle motion dynamics
UR - http://www.scopus.com/inward/record.url?scp=85147287776&partnerID=8YFLogxK
U2 - 10.1109/TMECH.2022.3233705
DO - 10.1109/TMECH.2022.3233705
M3 - Journal article
AN - SCOPUS:85147287776
SN - 1083-4435
VL - 28
SP - 668
EP - 680
JO - IEEE/ASME Transactions on Mechatronics
JF - IEEE/ASME Transactions on Mechatronics
IS - 2
ER -