@article{Zhou2025, 
author = {Yunfei Zhou and La Ta and Libin Ren and Mingxin Kang and Jiangyan Zhang and Yuhu Wu},
title = {An Adaptive MPC-based Motion Stability Control Strategy for Four In-Wheel Motor-Actuated Electric Vehicles},
year = {2025},
journal = {Unmanned Systems},
volume = {13},
number = {6},
pages = {1741-1753},
keywords = {optimization, Automotive control, predictive control for nonlinear systems, vehicle stability control},
url = {https://www.sciopen.com/article/10.1142/S2301385025430058},
doi = {10.1142/S2301385025430058},
abstract = {The popularization of electrified and intelligent vehicles presents novel challenges and opportunities for conventional vehicle control technologies. To solve the 62nd IEEE CDC benchmark challenge problem, this paper proposes an adaptive model predictive control-based dynamic stability control scheme for a four in-wheel motor-actuated electric vehicle. First, the longitudinal vehicle dynamic model including the four-wheel rotational dynamic model is presented. Considering the influence of road surface adhesion on tire force, a slip ratio adjustment torque controller is built by using a model predictive control algorithm, in which the optimal performance indexes comprehensively consider the energy consumption and longitudinal speed tracking accuracy. Then, an adaptive weighting factor is designed to balance the effect of the slip ratio adjustment torque on speed tracking and vertical stability based on the fuzzy control algorithm. To maintain vehicle motion stability, a motion adjustment torque controller based on the proportional integral derivative algorithm and the torque distribution scheme is designed. Finally, through co-simulation using Modelica/Simulink platform, simulation experiments are conducted under acceleration/braking conditions as well as ISO double lane change conditions. Simulation results demonstrate that the proposed control scheme effectively ensures both vehicle motion stability and accuracy in speed tracking and trajectory following.}
}