车辆多目标自适应巡航显式模型预测控制

赵树恩; 冷姚; 邵毅明

doi:10.19818/j.cnki.1671-1637.2020.03.019

车辆多目标自适应巡航显式模型预测控制

doi: 10.19818/j.cnki.1671-1637.2020.03.019

赵树恩^1,,
冷姚^1, ,,
邵毅明²

1.
重庆交通大学机电与车辆工程学院, 重庆 400074
2.
重庆交通大学交通运输学院, 重庆 400074

基金项目:

国家重点研发计划项目 2016YFB0100905

重庆市自然科学基金项目 cstc2018jcyjAX0422

详细信息

作者简介:
赵树恩(1972-), 男, 陕西洋县人, 重庆交通大学教授, 工学博士, 从事汽车主动安全技术研究

通讯作者:
冷姚(1995-), 男, 重庆人, 重庆交通大学工学硕士研究生

中图分类号: U461.2
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- 被引次数: 0
出版历程
- 收稿日期: 2019-12-13
- 刊出日期: 2020-06-25

Explicit model predictive control of multi-objective adaptive cruise of vehicle

1.
School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2.
School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Key Research and Development Project of China 2016YFB0100905

Natural Science Foundation of Chongqing cstc2018jcyjAX0422

More Information

Author Bio:
ZHAO Shu-en(1972-), male, professor, PhD, zse0916@163.com

Corresponding author: LENG Yao (1995-), male, graduate student, lengyao0025@163.com

摘要

摘要: 为了兼顾车辆自适应巡航控制(ACC)系统的跟踪控制效果和实时性, 提出了基于显式模型预测控制(EMPC)理论的车辆多目标自适应巡航控制方法; 基于车辆间运动学关系建立自适应巡航控制运动学模型, 根据预测控制理论推导预测时域内的跟踪误差预测模型, 并确定车辆安全性、跟踪性、经济性和舒适性等多性能目标函数和约束条件; 运用显式模型预测控制中的多参数规划理论, 将基于反复在线优化计算的闭环模型预测控制系统转化为与之等价的显式多面体分段仿射(PPWA)系统, 通过离线计算获得期望加速度与距离误差、速度误差、自车加速度和前车加速度等状态变量之间的最优控制律, 并设计在线查表的搜索流程, 通过定位当前状态所处分区, 并应用该分区的显式控制律实现自适应巡航控制; 进行了纵向跟踪工况仿真验证, 并与传统MPC-ACC控制方法进行对比。对比结果表明: 在前车正弦加减速工况下, EMPC-ACC控制器单步运算速度比MPC-ACC控制器平均提升了53.51%, EMPC-ACC控制下的平均距离跟踪误差为0.220 3 m, 平均速度误差为0.340 1 m·s^-1; 在前车阶跃加减速工况下, EMPC-ACC控制器单步运算速度比MPC-ACC控制器平均提升了72.96%, EMPC-ACC控制下的平均距离跟踪误差为0.331 9 m, 平均速度误差为0.399 1 m·s^-1。可见, 提出的EMPC-ACC控制算法在保证纵向跟踪性能的前提下, 有效地提高了自适应巡航控制的实时性。
- 汽车工程 /
- 自适应巡航控制 /
- 多性能目标优化 /
- 显式模型预测控制 /
- 多面体分段仿射 /
- 多参数二次规划
Abstract: In order to coordinate both the tracking control effect and real-time performance of adaptive cruise control(ACC) system, a multi-objective adaptive cruise control method of vehicle was proposed via the explicit model predictive control(EMPC) theory. Based on the kinematic relationship between vehicles, an adaptive cruise control kinematics model was established. The tracking error prediction model was derived in the forecast time domain by the predictive control theory. The multi-performance objective functions and constraints of vehicle safety, tracking, economy and comfort were determined. The closed-loop model predictive control system based on the repeated online optimization calculation, was transformed into an equivalent explicit polyhedral piece-wise affine(PPWA) system by the multi-parameter programming theory of explicit model predictive control. The optimal control laws from the distance error, velocity error, self-vehicle acceleration and rear vehicle acceleration to the desired acceleration were obtained by the off-line calculation. The search process of the online control was designed. The adaptive cruise control was realized by the explicit control laws in the partition of the current state vector. The longitudinal tracking conditions were simulated and verified, and the EMPC-ACC was compared with the traditional MPC-ACC. Compared result shows that in the sinusoidal acceleration and deceleration condition of lead vehicle, the single-step operation speed of EMPC-ACC controller improves by 53.51% on average compared with the MPC-ACC controller. Under the EMPC-ACC, the average distance tracking error is 0.220 3 m, and the average speed error is 0.340 1 m·s^-1. In the step acceleration and deceleration condition of lead vehicle, the single-step operation speed of EMPC-ACC controller improves by 72.96% on average compared with the MPC-ACC controller. The average distance tracking error is 0.331 9 m, and the average speed error is 0.399 1 m·s^-1 under the EMPC-ACC. It can be seen that on the premise of guaranteeing the longitudinal tracking performance, the proposed EMPC-ACC controller can effectively improve the real-time performance of the ACC.
- automotive engineering /
- adaptive cruise control /
- multi-performance-objective optimization /
- explicit model predictive control /
- polyhedral piece-wise affine /
- multi-parameter quadratic programming

HTML全文

图 1 车辆间运动学关系

Figure 1. Kinematics relationship between vehicles

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图 2 EMPC-ACC控制系统

Figure 2. EMPC-ACC control system

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图 3 参数分区截面

Figure 3. Parameter partition sections

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图 4 在线搜索流程

Figure 4. Online search flow

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图 5 仿真工况

Figure 5. Simulation conditions

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图 6 正弦工况仿真结果对比

Figure 6. Comparison of simulation results under sinusoidal condition

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图 7 阶跃工况仿真结果对比

Figure 7. Comparison of simulation results under step condition

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表 1 正弦工况下控制效果对比

Table 1. Comparison of control effects under sinusoidal condition

控制器距离误差/m 速度误差/(m·s^-1) 总耗时/s 平均单步耗时/ms

MPC-ACC 0.168 9 0.332 5 1.049 1 1.75

EMPC-ACC 0.220 3 0.340 1 0.681 3 1.14

下载: 导出CSV

表 2 阶跃工况控制效果对比

Table 2. Comparison of control effects under step condition

控制器距离误差/m 速度误差/(m·s^-1) 总耗时/s 平均单步耗时/ms

MPC-ACC 0.221 8 0.387 7 1.264 8 2.11

EMPC-ACC 0.331 9 0.399 1 0.733 8 1.22

下载: 导出CSV

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