智能车辆纵向控制策略分析

庞同嘉，王金波^*，田宇洋，张卫海

山东交通学院汽车工程学院，山东 济南  250357

摘要：为解决采用传统比例积分微分（proportional integral differential，PID）控制智能车辆纵向运行时自适应能力差、响应速度慢的问题，基于PID控制和模糊控制理论，采用MATLAB/Simulink搭建仿真控制模型，上控制器将期望速度与实际速度的偏差及偏差率作为仿真模型中模糊控制器的输入参数，按照模糊规则，输出比例、积分和微分因数，并与传统PID控制器算法加权，输出车辆期望加速度；下控制器通过加速制动标定表实现车辆驱动或制动切换。采用Carsim搭建车辆运行平台，将仿真控制模型与车辆运行平台联合，在期望车速为5、10、16和25 m/s时进行传统PID及模糊PID车辆纵向控制策略仿真对比。结果表明，传统PID控制策略平均速度偏差约为0.122 m/s，模糊PID控制策略平均速度偏差约为0.015 m/s；采用模糊PID控制策略，控制精度明显提高，且响应速度更快。

关键词：纵向运行；模糊PID；控制器；智能车辆

Analysis of longitudinal control strategy of the intelligent vehicle

PANG Tongjia, WANG Jinbo^*, TIAN Yuyang, ZHANG Weihai

School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China

Abstract: To address the issues of poor adaptability and slow response when using traditional proportional integral derivative (PID) control for the longitudinal operation of intelligent vehicles, based on combined PID control and fuzzy control theory, a simulation control model is established using MATLAB/Simulink, the upper controller take the deviation and rate of deviation between the desired speed and actual speed as input parameters for the fuzzy controller in the simulation model. Based on fuzzy rules, the upper controller outputs proportional, integral, and derivative coefficients, which are weighted with the traditional PID controller algorithm to produce the desired acceleration of the vehicle. The lower controller switches the vehicle′s drive or braking through an acceleration-braking calibration table. A vehicle operation platform is built using Carsim, and the simulation control model is integrated with the vehicle operation platform. Simulations comparing traditional PID and fuzzy PID longitudinal control strategies are conducted at desired speeds of 5, 10, 16, and 25 m/s. The results show that the average speed deviation of the traditional PID control strategy is approximately 0.122 m/s, while the average speed deviation of the fuzzy PID control strategy is about 0.015 m/s. The use of the fuzzy PID control strategy significantly improves control accuracy, and the response speed is faster.

Keywords: longitudinal movement; fuzzy-PID; controller; intelligent vehicle