基于模型预测控制的电动轮车辆横摆控制

为提高电动轮驱动车辆对不同路面的适应能力,基于模型预测控制提出一种将驱动电机的饱和输出力矩作为控制输入约束、将质心侧偏角作为输出约束的汽车横摆控制方法。建立2自由度的车辆状态空间模型作为预测模型,在线计算出跟踪理想横摆角速度所需的附加横摆力矩,通过调节相应驱动轮的驱动力来完成高效、简易的直接横摆力矩分配。将本文算法应用于四轮驱动的8自由度整车模型进行控制仿真,结果表明,该方法能够保证车辆在良好路面及湿滑路面上紧急转向和换道的操作稳定性,并能改善车辆循迹能力。

Yaw control for motor-wheel vehicle based on model predictive control

To improve the adaptability of vehicles with in-wheel motors to different road conditions, a yaw control method based on model predictive control was put forward, with saturated output torque of the motor and vehicle sideslip angle serving as control input and output constraints, respectively. A 2-DOF vehicle state-space model was built for the prediction, and the additional yaw moment required for tracking the ideal yaw rate was calculated online. Then efficient and simple allocation of the direct yaw moment was achieved by adjusting the driving forces of the corresponding driving wheels. The proposed algorithm was applied to an 8-DOF four-wheel drive vehicle model for control simulation. The results show that this control method can ensure the maneuvering stability in emergency steering and lane-changing of the vehicles not only on road with high adhesion coefficient but also on slippery road, and improve the vehicle tracking performance.