存在不确定性的网联自动驾驶车辆分布式模型预测控制

B. Homchaudhuri, Viranjan Bhattacharyya
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引用次数: 1

摘要

本文研究了多网联自动驾驶汽车的分布式鲁棒模型预测控制(MPC)方法,以确保其在存在不确定性的情况下安全运行。提出的分层控制框架包括参考轨迹生成、分布式鲁棒障碍占用集计算、分布式状态约束集评估、数据驱动线性模型表示和基于鲁棒管的MPC设计。为了实现自动驾驶汽车之间的分布式操作,我们提出了一种基于采样的参考轨迹生成方法和分布式约束集评估方法,将自动驾驶汽车之间的耦合避碰约束解耦。然后用数据驱动的线性模型表示非线性系统,求出非线性控制问题的凸等价。最后,为了保证在存在不确定性的情况下安全运行,本文采用了基于鲁棒管的MPC方法。对于一个多CAV变道问题,仿真结果表明了该控制器在计算效率和以分布式方式生成安全光滑CAV轨迹方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Model Predictive Control for Connected and Automated Vehicles in the Presence of Uncertainty
This paper focuses on the development of distributed robust model predictive control (MPC) methods for multiple connected and automated vehicles (CAVs) to ensure their safe operation in the presence of uncertainty. The proposed layered control framework includes reference trajectory generation, distributionally robust obstacle occupancy set computation, distributed state constraint set evaluation, data-driven linear model representation, and robust tube-based MPC design. To enable distributed operation among the CAVs, we present a method, which exploits sampling-based reference trajectory generation and distributed constraint set evaluation methods, that decouples the coupled collision avoidance constraint among the CAVs. This is followed by data-driven linear model representation of the nonlinear system to evaluate the convex equivalent of the nonlinear control problem. Finally, to ensure safe operation in the presence of uncertainty, this paper employs a robust tube-based MPC method. For a multiple CAV lane change problem, simulation results show the efficacy of the proposed controller in terms of computational efficiency and the ability to generate safe and smooth CAV trajectories in a distributed fashion.
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