TY - JOUR
T1 - Tube-based multi-objective robust model predictive steering and torque vectoring control of 4-IWD electric vehicles
AU - Tian, Ziang
AU - Li, Erhang
AU - Yu, Huilong
AU - Xi, Junqiang
N1 - Publisher Copyright:
© IMechE 2025.
PY - 2025
Y1 - 2025
N2 - The four-independent-wheel-drive electric vehicle is a typical over-actuated system. With the increase of actuators, the system complexity has brought great challenges to steering control and torque distribution. Existing works mostly employ model-based approaches since they are able to handle multiple control outputs while accommodating constraints. However, the investigated works usually focus on a single objective, and simplified prediction models are typically utilized in practice to mitigate computational burdens. In that case, it is difficult to fulfill the drivers’ diverse driving needs, and the robustness of the controllers is inevitably affected by the model mismatch. In this work, considering stability, motor energy loss, and tire slip energy, a multi-objective control framework is proposed. Furthermore, to address robustness against the model mismatch, robust model predictive control is devised. Compared with the state-of-the-art, the effectiveness of the proposed method has been validated in simulation. In the full-throttle acceleration scenario, the energy consumption is effectively suppressed. The motor energy loss and tire slip energy are reduced by 18.1% and 13.7%, respectively. Under the double lane change maneuver, vehicle stability is enhanced. The sideslip angle and yaw rate tracking errors are reduced by 12.5% and 16.1%.
AB - The four-independent-wheel-drive electric vehicle is a typical over-actuated system. With the increase of actuators, the system complexity has brought great challenges to steering control and torque distribution. Existing works mostly employ model-based approaches since they are able to handle multiple control outputs while accommodating constraints. However, the investigated works usually focus on a single objective, and simplified prediction models are typically utilized in practice to mitigate computational burdens. In that case, it is difficult to fulfill the drivers’ diverse driving needs, and the robustness of the controllers is inevitably affected by the model mismatch. In this work, considering stability, motor energy loss, and tire slip energy, a multi-objective control framework is proposed. Furthermore, to address robustness against the model mismatch, robust model predictive control is devised. Compared with the state-of-the-art, the effectiveness of the proposed method has been validated in simulation. In the full-throttle acceleration scenario, the energy consumption is effectively suppressed. The motor energy loss and tire slip energy are reduced by 18.1% and 13.7%, respectively. Under the double lane change maneuver, vehicle stability is enhanced. The sideslip angle and yaw rate tracking errors are reduced by 12.5% and 16.1%.
KW - four-independent-wheel drive electric vehicles
KW - Multi-objective optimization
KW - tire slip energy
KW - torque vectoring control
KW - tube-based robust model predictive control
UR - http://www.scopus.com/inward/record.url?scp=85214473905&partnerID=8YFLogxK
U2 - 10.1177/09544070241309139
DO - 10.1177/09544070241309139
M3 - Article
AN - SCOPUS:85214473905
SN - 0954-4070
JO - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
JF - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
ER -