中国机械工程

• 机械基础工程 • 上一篇    下一篇

智能汽车路径跟踪混合控制策略研究

蔡英凤1;李健2;孙晓强1;陈龙1;江浩斌2;何友国1;陈小波1   

  1. 1.江苏大学汽车工程研究院,镇江,212013
    2.江苏大学汽车与交通工程学院,镇江,212013
  • 出版日期:2020-02-10 发布日期:2020-04-13
  • 基金资助:
    国家重点研发计划资助项目(2017YFB0102603);
    国家自然科学基金资助项目(U1564201,U1664258,U1764257,61601203,61773184);
    江苏省重点研发计划资助项目(BE2016149);
    江苏省战略性新兴产业发展重大专项(苏发改高技发(2016)1094号,(2015)1084号);
    镇江市重点研发计划资助项目(GY2017006)

Research on Hybrid Control Strategy for Intelligent Vehicle Path Tracking

CAI Yingfeng1;LI Jian2;SUN Xiaoqiang1;CHEN Long1;JIANG Haobing2;HE Youguo1;CHEN Xiaobo1   

  1. 1.Jiangsu University Automotive Engineering Research Institute,Zhenjiang,Jiangsu,212013
    2.Department of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang,Jiangsu,212013
  • Online:2020-02-10 Published:2020-04-13

摘要:

针对传统单一控制算法无法有效协调智能汽车不同转向工况下横向控制性能要求的问题,根据智能汽车在高速和低速转向工况下呈现出的系统特性差异,设计了一种基于PID控制和模型预测控制的智能汽车路径跟踪混合控制策略。该控制策略在低速模式下采用PID控制,在高速模式下则采用模型预测控制,通过车辆速度确定路径跟踪控制模式,进而设计带稳定监督的控制模式切换机制,实现了横向控制系统的平滑切换。基于Carsim和MATLAB/Simulink仿真平台对所设计的智能汽车路径跟踪混合控制策略进行了仿真验证,在此基础上,进一步完成了实车试验。仿真和实车试验结果表明,所设计的混合控制策略能够保证智能汽车不同速度下的路径跟踪性能,具有较好的跟踪精度、实时性和车辆行驶稳定性。

关键词: 智能汽车, 路径跟踪, 车辆状态识别, 控制模式切换机制, 横向控制

Abstract: In order to solve the problems of traditional single control algorithm might not effectively coordinate the lateral control performance requirements of intelligent vehicles under different steering conditions, a hybrid control strategy of intelligent vehicle path tracking was designed based on PID control and model predictive control by combining with the different system characteristics of intelligent vehicles under high-speed and low-speed steering conditions. The control strategy selected PID control in low-speed mode and model predictive control in high-speed mode, The path following control mode was determined by vehicle speed, and then a switching mechanism of control mode with stable supervision was designed to realize smooth switching of lateral control system. Based on Carsim and MATLAB/Simulink simulation platform, the hybrid control strategy of intelligent vehicle path tracking was simulated and verified. Then, the real vehicle tests were further completed. Simulation and real vehicle test results show that the designed hybrid control strategy may ensure the performance of the intelligent vehicle path tracking at different speeds, and has good tracking accuracy, real-time performance and vehicle running stability.

Key words: intelligent vehicle, path tracking, vehicle status identification, control mode switching mechanism, lateral control

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