Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon
Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon
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DOI：
10.1109/tits.2022.3147719
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发表时间：
2022-01
影响因子：
8.5
通讯作者：
M. Ozkan;Yao Ma
中科院分区：
文献类型：
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作者：
M. Ozkan;Yao Ma
人类主导的卡车排成一排的系统已经被提出，以利用人类监督和车辆自主的好处。配备了人工引导和自主技术的人工主导的卡车排系统比全自动排系统更能灵活地处理不确定的交通条件。针对以人为主导的重型卡车车队，提出了一种新的分布式随机模型预测控制设计方法。所提出的DSMPC设计将人驾驶领队卡车的随机驾驶行为模型与排内跟随的自动卡车的分布式编队控制设计相结合。利用随机逆强化学习(SIRL)方法对人驾驶领头车的驾驶员行为进行学习。所提出的随机驾驶员行为模型旨在学习代表人类驾驶员行为的丰富性和唯一性的代价函数的分布，并给出一组特定于驾驶员的实例。分布式编队控制包括具有保证递归可行性、闭环机会约束满足和串稳定的串联DSMPC。通过仿真研究，考察了该设计在几种实际交通场景中的有效性。与基线队列控制策略(确定性分布式模型预测控制)相比，所提出的DSMPC在约束违反和间隔误差方面具有更好的控制器性能。
Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning systems are more versatile to handle uncertain traffic conditions than fully automated platooning systems. This paper presents a novel distributed stochastic model predictive control (DSMPC) design for a human-leading heavy-duty truck platoon. The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon. The driver behavior of the human-driven leader truck is learned by a stochastic inverse reinforcement learning (SIRL) approach. The proposed stochastic driver behavior model aims to learn a distribution of cost function, which represents the richness and uniqueness of human driver behaviors, with a given set of driver-specific demonstrations. The distributed formation control includes a serial DSMPC with guaranteed recursive feasibility, closed-loop chance constraint satisfaction, and string stability. Simulation studies are conducted to investigate the efficacy of the proposed design under several realistic traffic scenarios. Compared to the baseline platoon control strategy (deterministic distributed model predictive control), the proposed DSMPC achieves superior controller performance in constraint violations and spacing errors.