A delay-resistant cloud supported control model for Optimizing vehicle platooning operation

Ying Liu, Qing Xu, Guangwei Wang, Yi Liu, Mengchi Cai, Chaoyi Chen, Jianqiang Wang, Guodong Yin
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Abstract

The cloud supported system can effectively optimize vehicle platooning operation due to its centralized control mode in the cloud, but due to its wireless transmission characteristics and the complexity of the mixed traffic environment, the controlled traffic units will inevitably suffer from time delays and outside disturbances, which can lead to serious safety issues. To address the problem of platooning stable operation under stochastic road slope and bi-directional time-varying delay, a novel delay-resistant cloud supported control model is proposed in this paper. First, the mixed vehicle platoon system under the vehicle–road-cloud integrated architecture is established, considering the influence of driving intentions’ uncertainty of human-driven vehicles (HDVs), random variations of road slope, and bi-direction time-varying delay. Second, an exponential mean-square stable delay-dependent controller is designed to stabilize the cloud supported platoon system subject on the basis of robust H approach and Lyapunov-Krasovskii theorem. In addition, the inner-vehicle stability of time-delay mixed platoon system is analyzed using the enhanced free weighting matrix (EFWM) approach along with the improved cone complementarity linearization (ICCL) algorithm. Third, a L2 string stability criterion is defined to inhibit the increasement of perturbances as they propagate along the platoon. Finally, real traffic data as well as different driving conditions are adopted to verify the control performance of the presented method. Compared to traditional vehicle platoon control method, the presented controller can achieve better disturbance suppression and tracking performance under stochastic interferences and bi-direction time-varying delay, the distance error between adjacent vehicles is less than 0.44 m at low and medium speeds.
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用于优化车辆排队运行的抗延迟云支持控制模型
云支持系统由于采用云端集中控制模式,可以有效优化车辆的排队运行,但由于其无线传输特性和混合交通环境的复杂性,被控交通单元不可避免地会受到时间延迟和外界干扰的影响,从而导致严重的安全问题。针对随机道路坡度和双向时变延迟下的排车稳定运行问题,本文提出了一种新型的抗延迟云支持控制模型。首先,考虑了人驱车(HDV)驾驶意图不确定性、道路坡度随机变化和双向时变延迟的影响,建立了车路云一体化架构下的混合车排系统。其次,在鲁棒 H∞ 方法和 Lyapunov-Krasovskii 定理的基础上,设计了指数均方稳定延迟相关控制器,以稳定云支持的排车系统。此外,利用增强自由加权矩阵(EFWM)方法和改进的锥体互补线性化(ICCL)算法分析了时延混合排布系统的内部稳定性。第三,定义了 L2 字符串稳定性准则,以抑制扰动沿排传播时的增加。最后,采用真实交通数据和不同驾驶条件来验证所提出方法的控制性能。与传统的车辆排布控制方法相比,本文提出的控制器能在随机干扰和双向时变延迟条件下实现更好的扰动抑制和跟踪性能,中低速时相邻车辆间的距离误差小于 0.44 m。
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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