系统仿真学报 ›› 2020, Vol. 32 ›› Issue (8): 1556-1566.doi: 10.16182/j.issn1004731x.joss.18-0865

• 仿真模型/系统置信度评估技术 • 上一篇    下一篇

基于模型预测控制的智能车辆避障跟踪仿真

邓涛1,2, 李鑫1   

  1. 1.重庆交通大学 机电与车辆工程学院,重庆 400074;
    2.重庆交通大学 航空学院,重庆 400074
  • 收稿日期:2019-01-02 修回日期:2019-03-03 出版日期:2020-08-18 发布日期:2020-08-13
  • 作者简介:邓涛(1982-),男,江西新干,博士,教授,研究方向为新能源汽车理论与应用;李鑫(1995-),女,四川广安,硕士,研究方向为智能车辆运动控制。
  • 基金资助:
    国家自然科学基金(51305473),重庆市教委科学技术研究项目(KJQN201800718)

Simulation on Obstacle Avoidance Tracking of Intelligent Vehicle Based on Model Predictive Control

Deng Tao1,2, Li Xin1   

  1. 1. School of Mechatronics & Automobile Engineering, Chongqing Jiaotong University, Chongqing 400074, China;
    2. Institute of Aeronautics, Chongqing Jiaotong University, Chongqing 400074, China
  • Received:2019-01-02 Revised:2019-03-03 Online:2020-08-18 Published:2020-08-13

摘要: 针对智能车辆的避障跟踪控制问题,设计一种模型预测控制器。该控制器采用分层结构,上层基于非线性车辆点质量模型在避障时进行局部路径规划,下层基于单轨车辆动力学模型实现路径跟踪。通过Carsim和Matlab联合仿真,利用粒子群算法在线优化控制器参数,以不同的速度测试控制器性能,仿真结果表明:控制器具有良好的鲁棒性和实时性,跟踪误差较小,在单个和多个障碍物情况下,通过分层控制均实现了平稳避障并重新跟踪上原路径,表明控制器准确性高。

关键词: 智能车辆, 模型预测控制, 参数优化, 路径跟踪, 避障

Abstract: Aiming at the obstacle avoidance and path tracking control of intelligent vehicle, a model predictive controller (MPC) is designed. A layered structure is used in the controller. The high-level layer carries out the local path planning based on non-linear vehicle point mass model and the low-level layer realizes the path tracking based on monorail vehicle dynamics model. Through the joint simulation of Carsim and Matlab software, the controller parameters are optimized on-line by using Particle Swarm Optimization, and the performance of the controller is tested at different speeds. Simulation results show that the controller has good robustness, real-time performance and small tracking error. In the case of single and multiple obstacles, the hierarchical controller could avoid obstacles smoothly and re-track the original path in high accuracy.

Key words: intelligent vehicle, MPC, parameter optimization, path tracking, obstacle avoidance

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