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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Data-Driven Adaptive Optimal Control of Connected Vehicles
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Data-Driven Adaptive Optimal Control of Connected Vehicles

机译:互联车辆数据驱动的自适应最优控制

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摘要

In this paper, a data-driven non-model-based approach is proposed for the adaptive optimal control of a class of connected vehicles that is composed of n human-driven vehicles only transmitting motional data and an autonomous vehicle in the tail receiving the broadcasted data from preceding vehicles by wireless vehicle-to-vehicle (V2V) communication devices. Considering the cases of range-limited V2V communication and input saturation, several optimal control problems are formulated to minimize the errors of distance and velocity and to optimize the fuel usage. By employing an adaptive dynamic programming technique, the optimal controllers are obtained without relying on the knowledge of system dynamics. The effectiveness of the proposed approaches is demonstrated via the online learning control of the connected vehicles in Paramics' traffic microsimulation.
机译:本文提出了一种基于数据驱动的非基于模型的方法来对一类连接的车辆进行自适应最优控制,该方法由n个仅发送运动数据的人力驱动车辆和尾部接收广播的自动驾驶车辆组成无线车载通信(V2V)通信设备从先前的车辆获取数据。考虑到范围限制的V2V通信和输入饱和的情况,制定了一些最佳控制问题,以最小化距离和速度的误差并优化燃料使用。通过采用自适应动态编程技术,无需依赖系统动力学知识即可获得最佳控制器。通过在Paramics交通微仿真中对连接的车辆进行在线学习控制,证明了所提出方法的有效性。

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