Vehicle platoon transportation stands poised to effectively bolster both the safety and efficiency of vehicles, thereby mitigating traffic congestion and curtailing pollution. Nevertheless, the presence of heterogeneity and the dynamic nature inherent in vehicle platoons present formidable challenges to their safe and efficient control. To bolster the flexibility and scalability of vehicle platoons while aligning with the safety, followability, economy, and comfort prerequisites of each vehicle within the platoon, this paper zeroes in on heterogeneous commercial vehicle platoons featuring dynamic vehicle insertion or departure, proffering a multi-objective optimization control strategy grounded in a distributed model predictive control algorithm. Initially, accounting for the disparities in the dynamic characteristics of heterogeneous vehicle platoons, a vehicle platoon model grounded in the four-element model architecture is delineated. Subsequently, a maneuvering control strategy is advanced for dynamic vehicle platoons, aiming to safeguard the flexibility, scalability, and safety of the platoon amidst dynamic vehicle insertion or departure. Subsequently, a multi-objective optimization control scheme, orchestrated between the lead and follower vehicles, is put forth, predicated on the distributed model predictive control methodology. Lastly, the effectiveness of the designed control strategy under various unidirectional topology communication scenarios is scrutinized and corroborated via collaborative simulations employing Matlab/Simulink and TruckSim. The findings underscore the efficacy of the proposed control strategy in attaining multi-objective control of dynamic vehicle platoons, thereby amplifying their flexibility and scalability.
ShladoverSE.Connected and automated vehicle systems: introduction and overview. J Intell Transp Syst2018; 22(3): 190–200.
2.
DingJLiLPengH, et al. A rule-based cooperative merging strategy for connected and automated vehicles. IEEE Trans Intell Transp Syst2020; 21(8): 3436–3446.
3.
LiuZCaiYWangH, et al. Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions. IEEE Trans Intell Transp Syst2022; 23(7): 6640–6653.
4.
ShladoverSEDesoerCAHedrickJK, et al. Automatic vehicle control developments in the PATH program. IEEE Trans Veh Technol1991; 40(1): 114–130.
5.
ZhengYLiSEWangJ, et al. Stability and scalability of homogeneous vehicular platoon: study on the influence of information flow topologies. IEEE Trans Intell Transp Syst2016; 17(1): 14–26.
6.
BianYZhengYRenW, et al. Reducing time headway for platooning of connected vehicles via V2V communication. Transp Res Part C Emerg Technol2019; 102: 87–105.
7.
ZhengYXuMWuS, et al. Development of connected and automated vehicle platoons with combined spacing policy. IEEE Trans Intell Transp Syst2023; 24(1): 596–614.
8.
LiYLiHHuS, et al. Variable time headway policy based platoon control for heterogeneous connected vehicles with external disturbances. IEEE Trans Intell Transp Syst2022; 23(11): 21190–21200.
9.
LinFFardadMJovanovicMR.Optimal control of vehicular formations with nearest neighbor interactions. IEEE Trans Autom Control2012; 57(9): 2203–2218.
10.
BarooahPMehtaPGHespanhaJP.Mistuning-based control design to improve closed-loop stability margin of vehicular platoons. IEEE Trans Autom Control2009; 54(9): 2100–2113.
11.
HaoHBarooahP.Stability and robustness of large platoons of vehicles with double-integrator models and nearest neighbor interaction. Int J Robust Nonlinear Control2013; 23(18): 2097–2122.
12.
WangQGuoGCaiB.Distributed receding horizon control for fuel-efficient and safe vehicle platooning. Sci China Technol Sci2016; 59(12): 1953–1962.
13.
XingHPloegJNijmeijerH.Compensation of communication delays in a cooperative ACC system. IEEE Trans Veh Technol2020; 69(2): 1177–1189.
14.
ShawEHedrickJK. String stability analysis for heterogeneous vehicle strings. In: 2007 American control conference, New York, NY, USA, 9–13 July 2007, pp.3118–3125. New York: IEEE.
15.
CaiYYuXWangH, et al. Hybrid physics and neural network model for lateral vehicle dynamic state prediction. Proc IMechE, Part D: J Automobile Engineering2024; 238(1): 3–17.
16.
FangPCaiYChenL, et al. A high-performance neural network vehicle dynamics model for trajectory tracking control. Proc IMechE, Part D: J Automobile Engineering2023; 237(7): 1695–1709.
17.
WuXQinGYuH, et al. Using improved chaotic ant swarm to tune PID controller on cooperative adaptive cruise control. Optik2016; 127(6): 3445–3450.
18.
WangWCuiKGuL, et al. Cooperative adaptive cruise control using delay-based spacing policy: a robust adaptive non-singular terminal sliding mode approach. J Shanghai Jiaotong Univ Sci2021; 26(5): 634–646.
19.
GeJIOroszG.Optimal control of connected vehicle systems with communication delay and driver reaction time. IEEE Trans Intell Transp Syst2017; 18(8): 2056–2070.
20.
ZhuYZhaoDZhongZ.Adaptive optimal control of heterogeneous CACC system with uncertain dynamics. IEEE Trans Control Syst Technol2019; 27(4): 1772–1779.
21.
KazemiHMahjoubHNTahmasbi-SarvestaniA, et al. A learning-based stochastic MPC design for cooperative adaptive cruise control to handle interfering vehicles. IEEE Trans Intell Veh2018; 3(3): 266–275.
22.
ZhouYWangMAhnS.Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability. Transp Res Part B Methodol2019; 128: 69–86.
23.
GongSDuL.Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles. Transp Res Part B Methodol2018; 116: 25–61.
24.
TurriVBesselinkBJohanssonKH.Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning. IEEE Trans Control Syst Technol2017; 25(1): 12–28.
25.
YanMMaWZuoL, et al. Dual-mode distributed model predictive control for platooning of connected vehicles with nonlinear dynamics. Int J Control Autom Syst2019; 17(12): 3091–3101.
26.
YuSChenHFengY, et al. Nash optimality based distributed model predictive control for vehicle platoon. IFAC Pap OnLine2020; 53(2): 6610–6615.
27.
LiangyeLDefengHShimingY, et al. Distributed predictive control for string stability of heterogonous vehicle platoons with multiple communication topologies. In: 2019 Chinese control conference (CCC), Guangzhou, China, 27–30 July 2019, pp.2930–2935. New York: IEEE.
28.
LuSCaiYChenL, et al. Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning. IET Intell Trans Syst2023; 17(10): 1951–1963.
29.
BianYDuCHuM, et al. Fuel economy optimization for platooning vehicle swarms via distributed economic model predictive control. IEEE Trans Autom Sci Eng2022; 14(9): 2711–2723.
30.
HuMLiCBianY, et al. Fuel economy-oriented vehicle platoon control using economic model predictive control. IEEE Trans Intell Transp Syst2022; 23(11): 20836–20849.
31.
LiKBianYLiSE, et al. Distributed model predictive control of multi-vehicle systems with switching communication topologies. Transp Res Part C Emerg Technol2020; 118: 102717.
32.
BasiriMHGhojoghBAzadNL, et al. Distributed nonlinear model predictive control and metric learning for heterogeneous vehicle platooning with cut-in/cut-out maneuvers. In: 2020 59th IEEE conference on decision and control (CDC), Jeju, South Korea, 14–18 December 2020, pp.2849–2856. New York: IEEE.
33.
CapuanoASpanoMMusaA, et al. Development of an adaptive model predictive control for platooning safety in battery electric vehicles. Energies2021; 14(17): 5291.
34.
LiuPKurtAOzgunerU.Distributed model predictive control for cooperative and flexible vehicle platooning. IEEE Trans Control Syst Technol2019; 27(3): 1115–1128.
35.
LiuHZhuangWYinG, et al. Strategy for heterogeneous vehicular platoons merging in automated highway system. In: 2018 Chinese control and decision conference (CCDC), Shenyang, China, 9–11 June 2018, pp.2736–2740. New York: IEEE.
36.
GoliMEskandarianA. MPC-based lateral controller with look-ahead design for autonomous multi-vehicle merging into platoon. In: 2019 American control conference (ACC), Philadelphia, PA, USA, 10–12 July 2019, pp.5284–5291. New York: IEEE.
37.
GeXHanQLWangJ, et al. Scalable and resilient platooning control of cooperative automated vehicles. IEEE Trans Veh Technol2022; 71(4): 3595–3608.
38.
KazemiASharifiITalebiHA.Longitudinal and lateral control of vehicle platoons using Laguerre-based and robust MPC with merge and exit maneuvers. Control Eng Pract2024; 142: 105737.
39.
WeiHAparowVRHuBB, et al. A hierarchical distributed coordination framework for flexible and resilient vehicle platooning. IEEE Trans Intell Transp Syst2024; 25(6): 6090–6105.
40.
HuMBuLBianY, et al. Hierarchical cooperative control of connected vehicles: from heterogeneous parameters to heterogeneous structures. IEEE CAA J Autom Sin2022; 9(9): 1590–1602.
41.
ZhuYLiYJiaoA, et al. Hierarchical control of connected vehicle platoon by simultaneously considering the vehicle kinematics and dynamics. IEEE Trans Intell Veh2024; 9(1): 1333–1345.
42.
WangLBianYCaoD, et al. Hierarchical safe control of heterogeneous connected vehicle systems using adaptive fault-tolerant control. IEEE Trans Veh Technol2024; 73: 14313–14325.
