基于自适应变参数MPC的分布式驱动智能车轨迹跟踪控制

doi:10.3901/JME.2024.06.363

机械工程学报 ›› 2024, Vol. 60 ›› Issue (6): 363-377.doi: 10.3901/JME.2024.06.363

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基于自适应变参数MPC的分布式驱动智能车轨迹跟踪控制

杨泽坤^1,2, 李韶华², 王振峰³

1. 湖南大学机械与运载工程学院长沙 410082;
2. 石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室石家庄 050043;
3. 中汽研(天津)汽车工程研究院有限公司天津 300300

收稿日期:2023-04-05 修回日期:2023-11-26 出版日期:2024-03-20 发布日期:2024-06-07
通讯作者: 李韶华，女，1973年出生，博士，教授，博士研究生导师。主要研究方向为车辆系统动力学与控制。E-mail：lishaohua@stdu.edu.cn
作者简介:杨泽坤，男，1996年出生。主要研究方向为车辆系统动力学与控制。E-mail：yzk@stdu.edu.cn;王振峰，男，1987年出生，博士，高级工程师。主要研究方向为智能线控底盘与控制器开发。E-mail：wangzhenfeng44827@163.com
基金资助:
国家自然科学基金(U22A20246)、河北省重点研发计划(21342202D)和河北省省级科技计划(225676162GH)资助项目。

Trajectory Tracking Control of Distributed Driving Intelligent Vehicles Based on Adaptive Variable Parameter MPC

YANG Zekun^1,2, LI Shaohua², WANG Zhenfeng³

1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082;
2. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043;
3. CATARC(Tianjin) Automotive Engineering Research Institute Co., Ltd., Tianjing 300300

Received:2023-04-05 Revised:2023-11-26 Online:2024-03-20 Published:2024-06-07

摘要/Abstract

摘要： 为了协调分布式驱动智能驾驶车辆的轨迹跟踪精确性和稳定性，提高控制算法对车速扰动和路面附着系数变化等不确定性因素的自适应能力，基于平方根容积卡尔曼滤波(Square rooting cubature Kalman filter，SRCKF)估计轮胎侧向力以在线修正轮胎侧偏刚度，并基于T-S模糊变权重的MPC控制策略以实现轨迹跟踪控制。针对分布式驱动智能车全轮独立可控的特点，以前轮转角和各轮纵向驱动力为控制变量，以实时横向误差和横摆角误差为模糊输入，通过T-S模糊控制在线优化MPC目标函数权重，协调权重矩阵对轨迹跟踪精确性和稳定性的影响。通过仿真和试验数据，验证所提控制策略在多种工况下的有效性。研究表明，相比于传统MPC控制，所提出的自适应变参数MPC(Adaptive variable parameter MPC，AMPC)对80～120 km/h双移线湿滑路面、对接路面工况均有良好的跟踪效果，可有效提高轨迹跟踪精度，并能够协调控制跟踪精确性和稳定性，减少控制输出量的波动。

关键词: 智能驾驶, 轨迹跟踪, 轮胎力估计, 分布式驱动, 工况自适应

Abstract: To coordinate the trajectory tracking accuracy and stability of distributed drive intelligent driving vehicles and improve the adaptive capability of the control algorithm to uncertainties such as speed perturbations or road surface adhesion coefficient changes, the square rooting cubature Kalman filter (SRCKF) based tire lateral force estimation is used to online correct the tire cornering stiffness. The MPC control strategy based on T-S fuzzy variable weight is proposed to realize the trajectory tracking control. The front wheel steering angle and longitudinal drive force of each wheel are used as control variables under the characteristics of distributed drive intelligent vehicle with all-wheel independent controllability. The real-time lateral error and yaw error are used as fuzzy inputs to optimize the MPC objective function weights online by T-S fuzzy control and coordinate the influence of the weight matrix on the trajectory tracking accuracy and stability. The effectiveness of the proposed control strategy under various operating conditions is verified by simulation and experimental data. It is shown that compared with the traditional MPC control, the proposed adaptive variable parameter MPC (AMPC) has good tracking effect on 80-120 km/h double lane change, wet road and docking road conditions. AMPC can effectively improve the trajectory tracking accuracy, and can coordinate the control tracking accuracy and stability, meanwhile reduce the fluctuation of control output volume.

Key words: Intelligent driving, trajectory tracking, tire force estimation, distributed driving, condition adaption

中图分类号:

U461

杨泽坤, 李韶华, 王振峰. 基于自适应变参数MPC的分布式驱动智能车轨迹跟踪控制[J]. 机械工程学报, 2024, 60(6): 363-377.

YANG Zekun, LI Shaohua, WANG Zhenfeng. Trajectory Tracking Control of Distributed Driving Intelligent Vehicles Based on Adaptive Variable Parameter MPC[J]. Journal of Mechanical Engineering, 2024, 60(6): 363-377.

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基于自适应变参数MPC的分布式驱动智能车轨迹跟踪控制

Trajectory Tracking Control of Distributed Driving Intelligent Vehicles Based on Adaptive Variable Parameter MPC

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