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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (3): 485-493    DOI: 10.3785/j.issn.1008-973X.2022.03.007
计算机与控制工程     
基于数据驱动的鲁棒反步自适应巡航控制
宋家成1(),闫茂德1,*(),杨盼盼1,巨永锋1,岳靖斐2
1. 长安大学 电子与控制工程学院，陕西 西安 710064
2. 陕西汽车控股集团有限公司，陕西 西安 710200
Robust backstepping adaptive cruise control based on data-driven
Jia-cheng SONG1(),Mao-de YAN1,*(),Pan-pan YANG1,Yong-feng JU1,Jing-fei YUE2
1. School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
2. Shaanxi Automobile Holding Group Limited Company, Xi’an 710200, China
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摘要：

为了实现高精度的鲁棒自适应巡航控制（ACC），提出基于数据驱动的鲁棒反步自适应巡航控制算法. 利用反步技术设计虚拟控制器，将车辆间距控制转化为速度控制，避免速度相关型间距策略带来的间距与速度控制耦合；构建基于数据的耦合滑模面并设计状态观测器，补偿车辆复杂的非线性动力学特性、离散误差及外部干扰，提升控制算法的鲁棒性；利用反馈控制及鲁棒控制技术设计数据驱动的ACC鲁棒控制算法；分别选取固定时间间距、变时间间距策略，利用所提ACC算法及基于比例积分（PI）的ACC算法进行车辆自适应巡航控制对比仿真验证. 对比实验结果表明，所提算法在控制精度、鲁棒性方面具有优越性.
关键词： 自适应巡航控制;  数据驱动;  鲁棒控制;  反步控制;  间距策略    
Abstract:

A data-driven robust backstepping adaptive cruise control (ACC) algorithm was proposed to achieve high-precision robust ACC. First of all, a virtual controller was designed to convert the vehicle spacing control into speed control by using the backstepping technology, avoiding the coupling of speed and spacing control brought by the speed-dependent spacing strategy. Secondly, a data-based coupling sliding mode surface and state observer were constructed to compensate for the vehicle’s complex nonlinear dynamics, the discrete errors and the external disturbances, and the robustness of the control algorithm was improved. Then, a data-driven robust ACC algorithm was designed using feedback control and robust control techniques. Finally, the proposed ACC algorithm and the PI-based ACC were used for vehicle adaptive cruise tracking simulation verification with two spacing strategies (constant time headway and varying time headway). The superiority of the designed algorithm in terms of control accuracy and robustness are shown by the experimental results.
Key words: adaptive cruise control (ACC)    data-driven    robust control    backstepping control    spacing strategy
收稿日期: 2021-04-12 出版日期: 2022-03-29
CLC:  U 46  
基金资助: 国家自然科学基金资助项目（61803040）；陕西省重点研发计划资助项目（2019GY-218）；中央高校基本科研业务费资助项目（300102320720）
通讯作者: 闫茂德     E-mail: jiacheng.song@chd.edu.cn;mdyan@chd.edu.cn
作者简介: 宋家成（1993—），男，博士生，从事智能网联车辆控制研究. orcid.org/0000-0003-3183-123X. E-mail： jiacheng.song@chd.edu.cn
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引用本文:

宋家成,闫茂德,杨盼盼,巨永锋,岳靖斐. 基于数据驱动的鲁棒反步自适应巡航控制[J]. 浙江大学学报(工学版), 2022, 56(3): 485-493.

Jia-cheng SONG,Mao-de YAN,Pan-pan YANG,Yong-feng JU,Jing-fei YUE. Robust backstepping adaptive cruise control based on data-driven. Journal of ZheJiang University (Engineering Science), 2022, 56(3): 485-493.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.03.007    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I3/485

图 1  领航车辆的时间–速度–位置轨迹图
参数 数值 参数 数值
$ m/{\text{kg}} $ 1250+250 sin (t) $ {\rho _{\text{a}}}/\left( {{\text{N}} \cdot {{\text{s}}^{\text{2}}} \cdot {{\text{m}}^{ - 4}}} \right) $ 1.225 8
$ {A_{\text{f}}}/{{\text{m}}^2} $ 2.2 $ {\gamma \mathord{\left/{\vphantom {\gamma {^ \circ }}} \right.} ({^ \circ })} $ 5
$ {c_{\text{r}}} $ 0.018+0.002 sin (t) $ \delta $ 1
$ {c_{\text{f}}} $ 0.35+0.05 sin (t) $ g/\left( {{\text{m·}}{{\text{s}}^{-2}}} \right) $ 9.8
表 1  车辆模型基本参数
图 2  固定时间间距策略下的仿真结果
图 3  时变时间间距策略下的仿真结果
算法 CTH VTH
$ {e_{80}}/{\rm{m}} $ $\Delta \bar v/({\rm{m \cdot {s^{ - 1}}} })$ $\Delta \bar a/({\rm{m \cdot {s^{ - 2}}}) }$ $ {e_{80}}/{\rm{m}} $ $\Delta \bar v/({\rm{m \cdot {s^{ - 1} }})}$ $\Delta \bar a/({\rm{m \cdot {s^{ - 2}}}) }$
PI 0.2 3.076 9 0.106 7 0.25 3.076 9 0.294 7
本研究
0.015 3.076 9 0.068 4 0.06 3.076 8 0.293 1
表 2  控制跟踪误差、速度变化率及加速度变化率
图 4  采样时间为0.1时固定时间间距策略下的仿真结果
图 5  采样时间为0.1时变时间间距策略下的仿真结果
图 6  车辆切入工况下的仿真结果
图 7  目标车辆驶离工况下的仿真结果
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