摘要翻译：
路径跟踪和横向稳定性是自动驾驶汽车的关键问题。此外，由于铰接式重型车辆的操纵性差、尺寸大、质量变化大，这些问题在操纵时变得更加复杂。此外，质量的不确定性可能会显著降低系统的性能，甚至使系统不稳定。在控制器的设计过程中必须考虑这些参数的变化。然而，鲁棒控制技术通常需要离线调整辅助整定参数，这是不切实际的，导致次优运行。因此，本文提出了一种基于鲁棒递推调节器的具有参数不确定性的自主铰接式重型车辆的路径跟踪和横向控制方法。所提出的控制器的主要优点是不依赖于整定参数的离线调整。假设有效载荷上存在参数不确定性，并使用$\mathcal{H}_{\infty}$控制器进行性能比较。在双车道变换操纵下，对两种控制器的性能进行了评估。仿真结果表明，该方法具有较好的鲁棒性、横向稳定性、行驶平顺性和安全性，是一种很有应用前景的控制技术。
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英文标题：
《Robust path-following control for articulated heavy-duty vehicles》
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作者：
Filipe Marques Barbosa, Lucas Barbosa Marcos, Maira Martins da Silva,
  Marco Henrique Terra and Valdir Grassi Jr
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最新提交年份：
2020
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Systems and Control        系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要：
  Path following and lateral stability are crucial issues for autonomous vehicles. Moreover, these problems increase in complexity when handling articulated heavy-duty vehicles due to their poor manoeuvrability, large sizes and mass variation. In addition, uncertainties on mass may have the potential to significantly decrease the performance of the system, even to the point of destabilising it. These parametric variations must be taken into account during the design of the controller. However, robust control techniques usually require offline adjustment of auxiliary tuning parameters, which is not practical, leading to sub-optimal operation. Hence, this paper presents an approach to path-following and lateral control for autonomous articulated heavy-duty vehicles subject to parametric uncertainties by using a robust recursive regulator. The main advantage of the proposed controller is that it does not depend on the offline adjustment of tuning parameters. Parametric uncertainties were assumed to be on the payload, and an $\mathcal{H}_{\infty}$ controller was used for performance comparison. The performance of both controllers is evaluated in a double lane-change manoeuvre. Simulation results showed that the proposed method had better performance in terms of robustness, lateral stability, driving smoothness and safety, which demonstrates that it is a very promising control technique for practical applications.
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PDF链接：
https://arxiv.org/pdf/1808.02189