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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Safe Optimization of Highway Traffic With Robust Model Predictive Control-Based Cooperative Adaptive Cruise Control
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Safe Optimization of Highway Traffic With Robust Model Predictive Control-Based Cooperative Adaptive Cruise Control

机译:基于鲁棒模型预测控制的协同自适应巡航控制安全优化公路交通

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摘要

Road traffic crashes have been the leading cause of death among young people. Most of these accidents occur when the driver becomes distracted due to fatigue or external factors. Vehicle platooning systems, as cooperative adaptive cruise control, are one of the results of efforts devoted to the development of technologies for decreasing the number of road crashes and fatalities. Previous studies have suggested that such systems improve up to 273% highway traffic throughput and over 15% of fuel consumption if the clearance between vehicles in this class of roads can be reduced to 2 m. In this paper, we propose an approach that guarantees a minimum safety distance between vehicles taking into account the overall system delays and braking capacity of each vehicle. An l∞-norm robust model predictive controller has been developed to guarantee the minimum safety distance is not violated due to uncertainties on the preceding vehicle behavior. A formulation for a lower bound clearance of vehicles inside a platoon is also proposed. Simulation results show the performance of the approach compared to a nominal controller when the system is subject to both modeled and unmodeled disturbances.
机译:道路交通事故一直是年轻人死亡的主要原因。这些事故大部分发生在驾驶员由于疲劳或外部因素而分心的时候。作为协同自适应巡航控制的车辆排系统,是致力于减少道路交通事故和死亡人数的技术发展的努力成果之一。以前的研究表明,如果可以将此类道路上的车辆之间的间隙减小到2 m,则此类系统最多可将高速公路的交通吞吐量提高273%,并将油耗提高15%以上。在本文中,我们提出了一种方法,该方法应考虑到整个系统的延迟和每辆车的制动能力,从而确保车辆之间的最小安全距离。已经开发了l-范数鲁棒模型预测控制器,以确保不会因先前车辆行为的不确定性而违反最小安全距离。还提出了一种用于排内车辆的下限间隙的公式。仿真结果表明,当系统同时受到模型和非模型干扰时,与标称控制器相比,该方法的性能更高。

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