驾驶机器人车辆横向轨迹跟踪的改进模型预测控制  

Improved Model Predictive Control for Lateral Trajectory Tracking of Driving Robot Vehicles

作　　者：王韬[1] 惠怡静 张庆余 赵磊[1] 牛文铁[1] WANG Tao;HUI Yijing;ZHANG Qingyu;ZHAO Lei;NIU Wentie(Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education,Tianjin University,Tianjin 300350,China;China Automobile Development United Investment Co.,Ltd.,Beijing 100070,China;Automotive Data of China(Tianjin)Co.,Ltd.,Tianjin 300300,China)

机构地区：[1]天津大学机构理论与装备设计教育部重点实验室,天津300350 [2]中发联投资有限公司,北京100070 [3]中汽数据(天津)有限公司,天津300300

出　　处：《机械科学与技术》2025年第3期389-398,共10页Mechanical Science and Technology for Aerospace Engineering

摘　　要：为实现较高速行驶情况下驾驶机器人驾驶车辆进行横向轨迹跟踪,设计了一种基于动态杂交粒子群算法求解的非线性模型预测控制器。首先,简化转向机器人与车辆转向系统,建立车辆动力学模型。其次,应用双参数精确罚函数法将非线性有约束优化函数转换为非线性无约束优化函数。在此基础上,将驾驶机器人作为下层控制器,应用动态杂交粒子群算法对优化函数进行求解。最后,基于Simulink/CarSim和RT-LAB硬件仿真,验证了改进的模型预测控制器具有较高的跟踪精度和行驶平稳性。In order to drive a vehicle using driving robot for lateral trajectory tracking at higher speed,a nonlinear model prediction controller based on the dynamic hybrid particle swarm algorithm solution is designed.Firstly,the steering robot and vehicle steering system are simplified,and the vehicle dynamics model is established.Then,the two-parameter precision penalty function method is used to convert the nonlinear constrained optimization function into a nonlinear unconstrained optimization function.On the basis of these above,the driving robot is used as the lower controller,and the dynamic hybrid particle swarm algorithm is used to solve the optimization function.Finally,based on the Simulink/CarSim and RT-LAB simulation,the improved model prediction controller is verified to have high tracking accuracy and driving smoothness.

关 键 词：模型预测控制 驾驶机器人 轨迹跟踪控制 粒子群算法 

分 类 号：U473.1[机械工程—车辆工程] TP273[交通运输工程—载运工具运用工程]