自动驾驶载货汽车速度轨迹模型预测跟踪控制方法  

A Model Predictive Tracking Control Study for Speed Trajectory of Autonomous Driving Trucks

作　　者：赵靖华[1,3] 管清捷 刘晓雪 解方喜[3] Zhao Jinghua;Guan Qingjie;Liu Xiaoxue;Xie Fangxi(Jilin Normal University,Siping 136000;Jilin Police College,Changchun 130123;National Key Laboratory of Automotive Chassis Integration and Bionics,Jilin University,Changchun 130022)

机构地区：[1]吉林师范大学,四平136000 [2]吉林警察学院,长春130123 [3]吉林大学,汽车底盘集成与仿生全国重点实验室,长春130022

出　　处：《汽车技术》2024年第9期10-17,共8页Automobile Technology

基　　金：国家自然科学基金项目(61773009);吉林省科技发展计划项目(20240601034RC)。

摘　　要：为避免车辆质量变化与道路坡度对自动驾驶载货汽车纵向车速控制的干扰,通过智能导航系统获取车辆速度轨迹及道路坡度信息,建立车辆纵向动力学模型和压缩天然气(CNG)发动机动力学模型,并基于模型预测控制(MPC)框架设计了一种实时的动态规划(DP)速度轨迹跟踪控制器。仿真结果表明,在新欧洲驾驶循环(NEDC)和全球统一轻型车辆测试循环(WLTC)工况下,该控制器在载货汽车质量变化和道路坡度干扰条件下能使车速保持稳定,并可在优化速度跟踪误差的同时降低发动机天然气消耗量。In order to prevent vehicle mass changes and road slope interfering with longitudinal speed of autonomous driving truck,this article utilizes an intelligent navigation system to obtain information including vehicle speed trajectory and road slope.Vehicle longitudinal dynamic model and Compressed Natural Gas(CNG)engine dynamic model are established,and a real-time Dynamic Programming(DP)speed trajectory tracking controller is designed based on the Model Predictive Control(MPC)framework.The simulation results under NEDC and WLTC operating conditions show that the controller can keep vehicle speed stable under conditions of truck mass change and road slope interference,and can optimize speed tracking error while reducing natural gas consumption.

关 键 词：自动驾驶 纵向车速控制 模型预测控制 实时动态规划 道路坡度 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]