基于DMPC的智能汽车协同式自适应巡航控制

doi:10.19562/j.chinasae.qcgc.2021.08.008

汽车工程 ›› 2021, Vol. 43 ›› Issue (8): 1177-1186.doi: 10.19562/j.chinasae.qcgc.2021.08.008

基于DMPC的智能汽车协同式自适应巡航控制

鲁若宇^1,^2,³,胡杰^1,^2,³(),陈瑞楠^1,^2,³,徐文才^1,^2,³,曹恺⁴

^1.武汉理工大学，现代汽车零部件技术湖北省重点实验室，武汉 430070
^2.武汉理工大学，汽车零部件技术湖北省协同创新中心，武汉 430070
^3.新能源与智能网联车湖北工程技术研究中心，武汉 430070
^4.东风汽车公司技术中心，武汉 430058

收稿日期:2021-03-12 修回日期:2021-04-16 出版日期:2021-08-25 发布日期:2021-08-20
通讯作者: 胡杰 E-mail:auto_hj@163.com
基金资助:
湖北省技术创新专项(2019AEA169);湖北省科技重大专项(2020AAA001)

Cooperative Adaptive Cruise Control of Intelligent Vehicles Based on DMPC

Ruoyu Lu^1,^2,³,Jie Hu^1,^2,³(),Ruinan Chen^1,^2,³,Wencai Xu^1,^2,³,Kai Cao⁴

^1.Wuhan University of Technology，Hubei Key Laboratory of Modern Auto Parts Technology，Wuhan 430070
^2.Wuhan University of Technology，Auto Parts Technology Hubei Collaborative Innovation Center，Wuhan 430070
^3.Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering，Wuhan 430070
^4.Dongfeng Motor Corporation Technology Center，Wuhan 430058

Received:2021-03-12 Revised:2021-04-16 Online:2021-08-25 Published:2021-08-20
Contact: Jie Hu E-mail:auto_hj@163.com

摘要/Abstract

摘要：

本文中针对单向通信拓扑的非线性车辆队列协同式自适应巡航（CACC）控制问题，提出一种保证队列稳定且满足队列各车跟随性、安全性和乘员舒适性的分布式模型预测控制（DMPC）策略。首先建立了车辆队列的动力学模型和通信拓扑结构模型，并基于队列系统的多项优化性能设计代价函数和系统约束，使队列中每一辆跟随车基于其接收到的有限信息求解一个开环局部最优问题，计算出当前时刻的最优控制量作为输入并不断重复这个过程，达到滚动优化的目的，实现车辆队列的协同式自适应巡航控制。其次通过CACC系统局部代价函数之和构建Lyapunov候选函数，证明了车辆队列系统渐进稳定性的充分条件。最后通过CarSim和Simulink联合仿真，分析了算法在理想状态下对不同形式单向通信拓扑车辆队列的控制性能；通过实车试验，验证了算法在实车条件下感知层存在抖动、底层控制存在延迟和误差时的控制性能。仿真和实车试验的结果表明，本文提出的控制策略能使队列车辆实现各项优化性能，同时对外部干扰有较好的鲁棒性。

关键词: 智能汽车, 分布式模型预测控制, 协同式自适应巡航, 多目标优化

Abstract:

Aiming at the cooperative adaptive cruise control （CACC） problem of nonlinear vehicle platoon with unidirectional communication topology， a distributed model predictive control （DMPC） strategy is proposed in this paper， which ensures the stability of platoon and meets the requirements of vehicle following performance， safety and occupant comfort of each vehicle in the platoon. Firstly， the dynamic model and communication topology structure model of vehicle platoon are established， the cost function and system constraints are designed based on the multiple optimal performance of the platoon system， an open?looped local optimization problem is solved for each following vehicle in the platoon based on the limited information it receives， and the optimal control quantity at the current moment is calculated as the input. This process is repeated constantly to fulfill the goal of rolling optimization， achieving the CACC of the vehicle platoon. Then， the Lyapunov candidate function is constructed by the sum of local cost functions of CACC system， and the sufficient conditions for the asymptotic stability of vehicle platoon system are proved. Finally， a Carsim/Simulink joint simulation is conducted to analyze the control performance of the algorithm for vehicle platoon with different forms of one?way communication topology in ideal state， followed by a real vehicle test to verify the control performance of the algorithm in a real vehicle conditions of the jitter in perception layer and delay and error in the bottom control layer. Simulation and real vehicle test results show that the control strategy proposed can enable the vehicle platoon fulfill its optimized performances with better robustness to external disturbances.

Key words: intelligent vehicles, distributed model predictive control, cooperative adaptive cruise control, multi?objective optimization

鲁若宇,胡杰,陈瑞楠,徐文才,曹恺. 基于DMPC的智能汽车协同式自适应巡航控制[J]. 汽车工程, 2021, 43(8): 1177-1186.

Ruoyu Lu,Jie Hu,Ruinan Chen,Wencai Xu,Kai Cao. Cooperative Adaptive Cruise Control of Intelligent Vehicles Based on DMPC[J]. Automotive Engineering, 2021, 43(8): 1177-1186.

图/表 13

图1

表1

跟随车辆的基本参数"

车辆编号	$m i / k g$	$l i / m$	$η i$	$C A, i / (N ? s 2 ? m - 2)$	$f i$	$R i / m$
car1	1 650	3.05	0.89	1.15	0.032	0.38
car2	1 588	2.80	0.90	1.13	0.028	0.34
car3	1 430	2.75	0.86	1.12	0.039	0.30

表1

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参考文献 16

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基于DMPC的智能汽车协同式自适应巡航控制

Cooperative Adaptive Cruise Control of Intelligent Vehicles Based on DMPC

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