基于改变控制时域时间步长的智能车轨迹跟踪控制

doi:10.13229/j.cnki.jdxbgxb.20220542

吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (3): 620-630.doi: 10.13229/j.cnki.jdxbgxb.20220542

• 车辆工程·机械工程 • 上一篇

基于改变控制时域时间步长的智能车轨迹跟踪控制

谢宪毅^1,²(),王禹涵³,金立生¹(),赵鑫¹,郭柏苍¹,廖亚萍⁴,周彬⁵,李克强²

^1.燕山大学车辆与能源学院，河北秦皇岛 066004
^2.清华大学汽车安全与节能国家重点实验室，北京 100084
^3.中国第一汽车集团公司智能网联开发院，长春 130011
^4.北京航空航天大学特种车辆无人运输技术工业和信息化部重点实验室，北京 100191
^5.北京航空航天大学车路一体智能交通全国重点实验室，北京 100191

收稿日期:2022-04-27 出版日期:2024-03-01 发布日期:2024-04-18
通讯作者: 金立生 E-mail:xiexianyi123@126.com;jinls@ysu.edu.cn
作者简介:谢宪毅（1989-），男，讲师，博士.研究方向：智能车辆决策规划控制.E-mail：xiexianyi123@126.com
基金资助:
国家自然科学基金项目(52072333);汽车安全与节能国家重点实验室开放基金项目(KFY2211);河北省省级科技计划项目(F2021203107)

Intelligent vehicle trajectory tracking control based on adjusting step size of control horizon

Xian-yi XIE^1,²(),Yu-han WANG³,Li-sheng JIN¹(),Xin ZHAO¹,Bai-cang GUO¹,Ya-ping LIAO⁴,Bin ZHOU⁵,Ke-qiang LI²

^1.School of Vehicle and Energy，Yanshan University，Qinhuangdao 066004，China
^2.State Key Laboratory Automotive Safety and Energy，Tsinghua University，Beijing 100084，China
^3.Electrical and Electronics，General R&D Institute of China FAW Co. ，Ltd. ，Changchun 130011，China
^4.Key Laboratory of Unmanned Transportation Technology for Special Vehicles，Ministry of Industry and Information Technology，Beihang University，Beijing 100191，China
^5.National Key Laboratory of Vehicle-Road Integrated Intelligent Transportation，Beihang University，Beijing 100191，China

Received:2022-04-27 Online:2024-03-01 Published:2024-04-18
Contact: Li-sheng JIN E-mail:xiexianyi123@126.com;jinls@ysu.edu.cn

摘要/Abstract

摘要：

为了解决模型预测控制设计智能车轨迹跟踪控制器存在求解计算时间长、在线实时性低的问题，借助于矩阵分块化策略，提出了一种基于控制时域变步长的模型预测轨迹跟踪控制方法。通过矩阵分块化改变控制时域步长，并融入到二次规划的求解过程中，重构目标函数形式和系统约束条件，以减少求解过程中最优控制序列中待求解变量的数量，降低求解计算时间。在Simulink与Carsim联合仿真平台中，将本文方法与传统模型预测控制方法进行仿真对比分析。结果表明，相比于传统模型预测控制方法，本文方法在保证轨迹跟踪精度的前提下，平均求解计算时间降低了24.39%，最大单次计算时间降低了45.05%，采用“前密后疏”的分块矩阵，其控制器性能优于“平均化”的分块矩阵。

关键词: 车辆工程, 智能车辆, 控制时域变步长, 模型预测, 轨迹跟踪, 矩阵分块化

Abstract:

In order to solve the intelligent vehicle trajectory tracking controller based on model predictive control with long processing time and low real-time performance， a model predictive trajectory tracking control method based on variable step size in the control time domain was proposed using matrix-blocking strategy. The matrix-blocking method was used to change the control time domain step size and integrated into the quadratic programming solution process of model predictive control， and the objective function and system constraints were reconstructed to reduce the number of variables to be solved in the optimal control sequence during the solution process， and the calculation time are also be reduced. Based on Simulink and CarSim co-simulation platform， the proposed method was compared with the traditional model predictive control method. The results demonstrate that compared with the traditional model predictive control method， the proposed method not only reduces the average calculation time by 24.39% and the maximum single calculation time by 45.05%， but also ensuring the trajectory tracking accuracy. The performance of the controller using the dense before sparse block matrix is better than the average block matrix.

Key words: vehicle engineering, intelligent vehicle, adjusting the step size of control horizon, model predictive control, trajectory tracking, matrix blocking

中图分类号:

U461.6

谢宪毅,王禹涵,金立生,赵鑫,郭柏苍,廖亚萍,周彬,李克强. 基于改变控制时域时间步长的智能车轨迹跟踪控制[J]. 吉林大学学报(工学版), 2024, 54(3): 620-630.

Xian-yi XIE,Yu-han WANG,Li-sheng JIN,Xin ZHAO,Bai-cang GUO,Ya-ping LIAO,Bin ZHOU,Ke-qiang LI. Intelligent vehicle trajectory tracking control based on adjusting step size of control horizon[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 620-630.

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参数	数值
整车质量/kg	1704.7
横摆转动惯量/（kg·m²）	3048.1
侧倾转动惯量/（kg·m²）	744
簧载质量转动惯量积/（kg·m²）	21.09
前轴到质心的距离/m	1.035
后轴到质心的距离/m	1.655
前、后轴轮距/m	1.535
前轴侧倾刚度/（N·m·rad^-1）	2 328
后轴侧倾刚度/（N·m·rad^-1）	2 653
前轴侧倾阻尼/（N·m·s·rad^-1）	47 298
后轴侧倾阻尼/（N·m·s·rad^-1）	37 311
前轮侧偏刚度（N·rad^-1）	31 106.98
后轮侧偏刚度（N·rad^-1）	29 584.56
车轮转动惯量/（kg·m²）	0.9
车轮滚动半径/m	0.313

参数	基于分块矩阵 I₁的MPC	传统MPC
平均计算时间/s	0.0031	0.0041
单次最大计算时间/s	0.0061	0.0111

参数	基于分块矩阵 I₁的MPC	基于分块矩阵 I₃的MPC
平均计算时间/s	0.0031	0.0032
单次最大计算时间/s	0.0061	0.0062

参数	基于分块矩阵 I₁的MPC	传统MPC（N_c=4）
平均计算时间/s	0.003 11	0.003 34
单次最大计算时间/s	0.006 1	0.006 8

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基于改变控制时域时间步长的智能车轨迹跟踪控制

Intelligent vehicle trajectory tracking control based on adjusting step size of control horizon

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