车辆与动力技术

为提高智能汽车在高速工况下的路径跟踪精度，设计了基于MPC和模糊控制的轨迹跟踪控制器，基于MPC算法建立车辆单轨模型，通过控制前轮转角实现智能汽车对五阶多项式避障路径的跟踪；以横摆角速度及质心侧偏角误差值为双输入，通过模糊控制输出横摆力矩施加至车轮，以减少高速工况下轨迹的偏差.搭建CarSim/Simulink联合仿真模型进行验证，结果表明，文中的控制方法能减少高速工况下路径跟踪的偏差，跟踪效果良好.

We proposed a trajectory tracking controller based on model predictive control(MPC) and fuzzy control to improve the path tracking accuracy of an intelligent vehicle at high speed.The vehicle monorail model was established based on the MPC algorithm, then we controlled the front wheel angle of the intelligent vehicle to track the fifth order polynomial path to avoid obstacles.We took the yaw rate and sideslip error of the center of mass as two inputs and used fuzzy control to output the yaw moment applying to the wheel to reduce the trajectory deviation under high-speed conditions.The CarSim/Simulink co-simulation model was established to evaluate our proposed method.The results show a great tracking effect as the controller can reduce the deviation of path tracking under high-speed conditions.