城市道路智能网联车辆轨迹鲁棒控制方法

doi:10.16097/j.cnki.1009-6744.2024.04.004

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (4): 31-40.DOI: 10.16097/j.cnki.1009-6744.2024.04.004

• 智能交通系统与信息技术 • 上一篇下一篇

城市道路智能网联车辆轨迹鲁棒控制方法

刘美岐^*1a,1b，金楷然^1a ，李雅澜^1a,1b，郭戈^2,3

1. 大连海事大学，a.航运经济与管理学院，b.综合交通运输协同创新中心，辽宁大连116026；2.东北大学，流程工业综合自动化国家重点实验室，沈阳110819；3.东北大学秦皇岛分校，控制工程学院，河北秦皇岛066004

收稿日期:2024-04-04 修回日期:2024-04-26 接受日期:2024-05-08 出版日期:2024-08-25 发布日期:2024-08-21
作者简介:刘美岐(1992- )，女，辽宁辽阳人，讲师，博士。
基金资助:
国家自然科学基金青年科学基金(52202397)；中国博士后科学基金 (2023TQ0040)。

Robust Model Predictive Control of Connected and Automated Vehicle Trajectories on Urban Roads

LIU Meiqi^*1a,1b,JIN Kairan^1a,LI Yalan^1a,1b,GUO Ge^2,3

1a. School of Maritime Economics and Management, 1b. Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian 116026, Liaoning, China; 2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; 3. School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, Hebei, China

Received:2024-04-04 Revised:2024-04-26 Accepted:2024-05-08 Online:2024-08-25 Published:2024-08-21
Supported by:
Young Scientists Fund of the National Natural Science Foundation of China (52202397)；ChinaPostdoctoral Science Foundation (2023TQ0040)。

摘要/Abstract

摘要： 针对车辆控制系统的执行器延迟和外部环境不确定性导致的智能网联车队性能不稳定甚至系统失稳问题，本文提出一种强鲁棒的信控路段智能网联车辆轨迹优化控制方法。首先，构建三阶车辆控制系统动态模型，以优化乘坐舒适性、安全性、车队平稳性、燃油经济性和交通延误为目标，以闯红灯和不安全的车间距离为惩罚，以速度和加速度上下界为约束，以前方交叉口信号变化为系统反馈，设计车辆轨迹控制器以提升受控车辆的运行效率。其次，将车辆轨迹控制器构建为最小—最大模型预测控制问题，针对执行器延迟和不确定性的最坏情况优化成本函数得到控制输入，以改善受控车队的稳定性。然后，采用迭代的庞德里雅金极大值原理进行求解，将控制问题离散化并将不确定参数划分为多个连续区间，找出最坏情况进行迭代计算，正向求解状态变量并逆向求解共轭变量，以提高控制问题的求解效率。仿真结果表明，该轨迹控制方法在信控路段和无信控路段均表现良好，有效应对随机的执行器延迟和外部车辆的干扰，如信号参数的改变、人类驾驶车辆的剧烈变速和小幅度的轨迹偏差。本文提出的鲁棒控制器具有良好的稳定性和优越性，能够显著改善乘坐舒适度(75.7%)并减少燃油消耗(18.4%)。

关键词: 智能交通, 轨迹控制, 鲁棒模型预测控制, 智能网联车, 感应式信号

Abstract: To solve the problem of the actuator delay and uncertainties which may cause platoon instability or even destabilization, this paper proposes a robust model predictive control approach for vehicle trajectory optimization on urban roads. A third-order vehicle dynamics model was developed to optimize ride comfort, safety, platoon stability, fuel efficiency, and traffic delay. The behaviors of the red-light violations and the unsafe inter-vehicle distances were penalized, and the speed and acceleration were bounded. The signal changes were treated as system feedback. The proposed vehicle trajectory controller aims to improve the operational efficiency of controlled vehicles. The vehicle trajectory controller was formulated as a Min-Max model predictive control problem to enhance platoon stability by determining the control inputs in the worst case of actuator delays and uncertainties. Then, the iterative Pontryagin's maximum principle was used to solve the control problem, which discretized the control problem and divided the uncertain parameters into multiple intervals. To improve the computational efficiency, the proposed solution approach identified the worst case, iteratively computed the state variables forward in time, and solved the costate variables backward in time. The numerical simulation results demonstrate that the proposed controller performs well on the lane sections with and without signal controllers. The robust model predictive control approach can effectively response to random actuator delays and external vehicle disturbances, such as signal changes, abrupt speed changes, and small trajectory deviations caused by human drivers. The proposed robust Min-Max model predictive controller (MM-MPC) manifests better stability and superiority than the normal MPC controller in riding comfort (improved by 75.7%) and fuel consumption (reduced by 18.4%).

Key words: intelligent transportation, trajectory control, robust model predictive control, connected and automated vehicle, actuated signal control

中图分类号:

U491

刘美岐, 金楷然, 李雅澜, 郭戈. 城市道路智能网联车辆轨迹鲁棒控制方法[J]. 交通运输系统工程与信息, 2024, 24(4): 31-40.

LIU Meiqi, JIN Kairan, LI Yalan, GUO Ge. Robust Model Predictive Control of Connected and Automated Vehicle Trajectories on Urban Roads[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(4): 31-40.

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http://www.tseit.org.cn/CN/Y2024/V24/I4/31

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城市道路智能网联车辆轨迹鲁棒控制方法

Robust Model Predictive Control of Connected and Automated Vehicle Trajectories on Urban Roads

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