Peiyu Zhang
ABSTRACT
Vehicle platooning has gained significant attention due to its potential to enhance road safety and efficiency. Leveraging stochastic optimization methods, this paper presents a distributed Stochastic Model Predictive Control (SMPC) controller tailored for vehicle platooning systems to improve their safety and robustness. Uniquely, our methodology describes the vehicle's dynamic state and establishes the error equation for the platoon system founded on a mass-spring structure structural concept, a departure from existing models. Using this, we formulate an SMPC platoon control framework resilient to stochastic disturbances, effectively integrating desired objective and probabilistic chance constraints. Given the probabilistic information of the random perturbations, an equivalent, computationally efficient framework for the SMPC is deduced under a fixed distribution. Comprehensive simulation experiments serve to validate the efficacy of our proposed SMPC platoon controller.
- Siyuan Gong, Jinglai Shen, and Lili Du. 2016. Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon. Transportation Research Part B: Methodological 94 (2016), 314--334.Google Scholar
Cross Ref
- Edwin González, Javier Sanchis, Sergio García-Nieto, and José Salcedo. 2020. A comparative study of stochastic model predictive controllers. Electronics 9, 12 (2020), 2078.Google ScholarCross Ref
- Jacopo Guanetti, Yeojun Kim, and Francesco Borrelli. 2018. Control of connected and automated vehicles: State of the art and future challenges. Annual reviews in control 45 (2018), 18--40.Google Scholar
- Tor Aksel N Heirung, Joel A Paulson, Jared O'Leary, and Ali Mesbah. 2018. Stochastic model predictive control-how does it work? Computers & Chemical Engineering 114 (2018), 158--170.Google ScholarCross Ref
- Manjiang Hu, Chongkang Li, Yougang Bian, Hui Zhang, Zhaobo Qin, and Biao Xu. 2022. Fuel Economy-Oriented Vehicle Platoon Control Using Economic Model Predictive Control. IEEE Transactions on Intelligent Transportation Systems 23, 11 (2022), 20836--20849. https://doi.org/10.1109/TITS.2022.3183090Google ScholarCross Ref
- Xiaorong Hu, Lantao Xie, Lei Xie, Shan Lu, Weihua Xu, and Hongye Su. 2022. Distributed model predictive control for vehicle platoon with mixed disturbances and model uncertainties. IEEE Transactions on Intelligent Transportation Systems 23, 10 (2022), 17354--17365.Google ScholarCross Ref
- Dongyao Jia, Kejie Lu, Jianping Wang, Xiang Zhang, and Xuemin Shen. 2016. A Survey on Platoon-Based Vehicular Cyber-Physical Systems. IEEE Communications Surveys & Tutorials 18, 1 (2016), 263--284. https://doi.org/10.1109/COMST. 2015.2410831Google ScholarDigital Library
- Zhiyang Ju, Hui Zhang, and Ying Tan. 2021. Distributed stochastic model predictive control for heterogeneous vehicle platoons subject to modeling uncertainties. IEEE Intelligent Transportation Systems Magazine 14, 2 (2021), 25--40.Google ScholarCross Ref
- Shengbo Eben Li, Yang Zheng, Keqiang Li, Yujia Wu, J. Karl Hedrick, Feng Gao, and Hongwei Zhang. 2017. Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities. IEEE Intelligent Transportation Systems Magazine 9, 3 (2017), 46--58. https://doi.org/10. 1109/MITS.2017.2709781Google ScholarCross Ref
- Yongfu Li, Zhenyu Zhong, Yu Song, Qi Sun, Hao Sun, Simon Hu, and Yibing Wang. 2022. Longitudinal Platoon Control of Connected Vehicles: Analysis and Verification. IEEE Transactions on Intelligent Transportation Systems 23, 5 (2022), 4225--4235. https://doi.org/10.1109/TITS.2020.3042973Google ScholarDigital Library
- Matthias Lorenzen, Fabrizio Dabbene, Roberto Tempo, and Frank Allgöwer. 2016. Constraint-tightening and stability in stochastic model predictive control. IEEE Trans. Automat. Control 62, 7 (2016), 3165--3177.Google ScholarCross Ref
- David Q Mayne. 2014. Model predictive control: Recent developments and future promise. Automatica 50, 12 (2014), 2967--2986.Google ScholarDigital Library
- David Q Mayne, James B Rawlings, Christopher V Rao, and Pierre OM Scokaert. 2000. Constrained model predictive control: Stability and optimality. Automatica 36, 6 (2000), 789--814.Google ScholarDigital Library
- Sahand Mosharafian and Javad Mohammadpour Velni. 2023. A hybrid stochastic model predictive design approach for cooperative adaptive cruise control in connected vehicle applications. Control Engineering Practice 130 (2023), 105383.Google ScholarCross Ref
- Mehmet Fatih Ozkan and Yao Ma. 2022. Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon. IEEE Transactions on Intelligent Transportation Systems 23, 9 (2022), 16059--16071. https://doi.org/10. 1109/TITS.2022.3147719Google ScholarDigital Library
- Vahab Rostampour and Tamás Keviczky. 2018. Distributed Stochastic Model Predictive Control Synthesis for Large-Scale Uncertain Linear Systems. In 2018 Annual American Control Conference (ACC). 2071--2077. https://doi.org/10.23919/ ACC.2018.8431452Google Scholar
- Meng Wang, Winnie Daamen, Serge P. Hoogendoorn, and Bart van Arem. 2016. Cooperative Car-Following Control: Distributed Algorithm and Impact on Moving Jam Features. IEEE Transactions on Intelligent Transportation Systems 17, 5 (2016), 1459--1471. https://doi.org/10.1109/TITS.2015.2505674Google ScholarDigital Library
- Jianhua Yin, Dan Shen, Xiaoping Du, and Lingxi Li. 2022. Distributed Stochastic Model Predictive Control With Taguchi's Robustness for Vehicle Platooning. IEEE Transactions on Intelligent Transportation Systems 23, 9 (2022), 15967--15979. https://doi.org/10.1109/TITS.2022.3146715Google ScholarDigital Library
- Weiming Zhao, Dong Ngoduy, Simon Shepherd, Ronghui Liu, and Markos Papageorgiou. 2018. A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. Transportation Research Part C: Emerging Technologies 95 (2018), 802--821.Google ScholarCross Ref
- Jianshan Zhou, Daxin Tian, Zhengguo Sheng, Xuting Duan, Guixian Qu, Dezong Zhao, Dongpu Cao, and Xuemin Shen. 2022. Robust min-max model predictive vehicle platooning with causal disturbance feedback. IEEE Transactions on Intelligent Transportation Systems 23, 9 (2022), 15878--15897.Google ScholarDigital Library
Index Terms
- Robust Car-Following Control of Connected and Autonomous Vehicles: A Stochastic Model Predictive Control Approach
Recommendations
A smart car control model for brake comfort based on car following
This paper demonstrates a novel car-following model focused on passenger comfort, for example, a rapid deceleration will make passengers uncomfortable. The brake comfort model of car following was set up according to the relationship between vehicle ...
Autonomous Highway Car Following System Based on Fuzzy Control
HPCCT '18: Proceedings of the 2018 2nd High Performance Computing and Cluster Technologies ConferenceThe ability of controlling real-time vehicle status is extremely important for car-following systems. The status of the traffic stream on highways has impact on the vehicle control while in a high driving speed. In this paper, a feedback car-following ...
Proactive Longitudinal Control of Connected and Autonomous Vehicles with Lane-Change Assistance for Human-Driven Vehicles
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)Vehicle automation and connectivity enable the cooperative platooning control of connected and autonomous vehicles (CAVs), which can enhance traffic safety and alleviate traffic oscillations. However, CAVs and human-driven vehicles (HDVs) will coexist on ...
Comments