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多级故障下网联车队协同变道性能监督容错控制

付主木 琚函铮 夏元清 陶发展 张玮 陈启宏

付主木, 琚函铮, 夏元清, 陶发展, 张玮, 陈启宏. 多级故障下网联车队协同变道性能监督容错控制. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260085
引用本文: 付主木, 琚函铮, 夏元清, 陶发展, 张玮, 陈启宏. 多级故障下网联车队协同变道性能监督容错控制. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260085
Fu Zhu-Mu, Ju Han-Zheng, Xia Yuan-Qing, Tao Fa-Zhan, Zhang Wei, Chen Qi-Hong. Performance-monitored fault-tolerant control for cooperative lane-changing of connected vehicular platoons under multi-level failures. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260085
Citation: Fu Zhu-Mu, Ju Han-Zheng, Xia Yuan-Qing, Tao Fa-Zhan, Zhang Wei, Chen Qi-Hong. Performance-monitored fault-tolerant control for cooperative lane-changing of connected vehicular platoons under multi-level failures. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c260085

多级故障下网联车队协同变道性能监督容错控制

doi: 10.16383/j.aas.c260085 cstr: 32138.14.j.aas.c260085
基金项目: 国家自然科学基金(62371182, 62301212, U25A20460), 河南省高校科技创新人才计划(23HASTIT021), 河南省人才项目(264000510006)资助
详细信息
    作者简介:

    付主木:中原工学院自动化与电气工程学院教授. 主要研究方向为混合动力汽车系统建模与模式切换控制, 多智能体协同优化控制和自适应模糊控制. E-mail: fuzhumu@haust.edu.cn

    琚函铮:河南科技大学信息工程学院(人工智能学院)博士研究生. 主要研究方向为网联车队协同控制, 多智能体系统一致性控制和随机非线性系统控制. E-mail: jhz1521168686@163.com

    夏元清:中原工学院自动化与电气工程学院教授. 主要研究方向为云控制, 云数据中心优化调度管理, 智能交通, 模型预测控制, 自抗扰控制, 飞行器控制和空天地一体化网络协同控制. E-mail: xia_yuanqing@bit.edu.cn

    陶发展:河南科技大学信息工程学院(人工智能学院)教授. 主要研究方向为复杂动态系统鲁棒控制, 智能网联汽车协同优化和深度学习人工智能. 本文通信作者. E-mail: taofazhan@haust.edu.cn

    张玮:河南科技大学信息工程学院(人工智能学院)博士研究生. 主要研究方向为行车风险建模和自动驾驶轨迹规划. E-mail: zhangwehkj@163.com

    陈启宏:武汉理工大学自动化学院教授. 主要研究方向为新能源电力变换与控制, 燃料电池检测与控制, 预测控制. E-mail: chenqh@whut.edu.cn

  • 中图分类号: Y

Performance-monitored Fault-tolerant Control for Cooperative Lane-changing of Connected Vehicular Platoons Under Multi-level Failures

Funds: Supported by National Natural Science Foundation of China (62371182, 62301212, U25A20460), Program for Science and Technology Innovation Talents in the University of Henan Province (23HASTIT021), and Henan Provincial Talent Program (264000510006)
More Information
    Author Bio:

    FU Zhu-Mu Professor at the School of Automation and Electrical Engineering, Zhongyuan University of Technology. His research interests include system modeling and mode-switching control for hybrid electric vehicles, cooperative optimal control of multi-agent systems, and adaptive fuzzy control

    JU Han-Zheng Ph.D. candidate at the School of Information Engineering (School of Artificial Intelligence), Henan University of Science and Technology. His research interests include cooperative control of connected vehicular platoons, consensus control of multi-agent systems, and stochastic nonlinear system control

    XIA Yuan-Qing Professor at the School of Automation and Electrical Engineering, Zhongyuan University of Technology. His research interests include cloud control, cloud data center optimization scheduling and management, intelligent transportation, model predictive control, active disturbance rejection control, flight control and networked cooperative control for integration of space, air and earth

    TAO Fa-Zhan Professor at the School of Information Engineering (School of Artificial Intelligence), Henan University of Science and Technology. His research interests include robust control of complex dynamic systems, collaborative optimization of intelligent connected vehicles, and deep learning artificial intelligence. Corresponding author of this paper

    ZHANG Wei Ph.D. candidate at the School of Information Engineering (School of Artificial Intelligence), Henan University of Science and Technology. His research interests include driving risk modeling and trajectory planning for autonomous driving

    CHEN Qi-Hong Professor at the School of Automation, Wuhan University of Technology. His research interests include new energy power conversion and control, fuel cell detection and control, and predictive control

  • 摘要: 针对由涵盖执行器部分至完全失效的多级故障所导致的网联车队决策?执行层协调失配问题, 提出一种融合跟踪精度与收敛速度监督的协同变道容错控制方法. 首先, 针对变道决策规划参数不当引发的快速性与平稳性冲突问题, 设计一种基于可变安全时窗的目标轨迹规划机制, 规避相邻车道动态障碍车碰撞风险, 实现变道过程快速响应与乘客舒适性的动态权衡. 其次, 针对多级故障、饱和输入与欺骗攻击耦合造成的执行层轨迹跟踪失控问题, 构建一种集成分布式扰动近似技术的性能监督容错跟踪控制策略, 确保队列跟踪精度与收敛速度在预设时间内恢复到指定范围内, 消除对故障因子非零的强假设条件. 接着, 针对多级故障过渡阶段安全时窗非光滑突变引发的决策−执行层协调失配问题, 提出一种基于性能过渡映射的自适应协同变道控制方法, 确保决策层与执行层动态双向交互调节, 保障车队一致跟踪目标轨迹并安全完成变道. 最后, 通过仿真实验验证了所提方法的有效性.
  • 图  1  网联车队系统

    Fig.  1  Connected vehicular platoon system

    图  2  队列变道场景

    Fig.  2  Platoon lane-changing scenes

    图  3  协同变道控制方法框图

    Fig.  3  The block diagram of cooperative lane-changing control method

    图  4  领航车0与障碍车$ L_d $

    Fig.  4  Leader vehicle 0 and obstacle vehicle $ L_d $

    图  5  领航车0与障碍车$ L_o $

    Fig.  5  Leader vehicle 0 and obstacle vehicle $ L_o $

    图  6  跟随车$ n $与障碍车$ F_d $

    Fig.  6  Following vehicle $ n $ and obstacle vehicle $ F_d $

    图  7  跟随车$ n $与障碍车$ F_o $

    Fig.  7  Following vehicle $ n $ and obstacle vehicle $ F_o $

    图  8  变道过程的转弯半径

    Fig.  8  Turning radius during the lane-changing

    图  9  具有双冗余执行器的容错车辆结构

    Fig.  9  Architecture of fault-tolerant vehicle with dual-redundant actuators

    图  10  整个队列变道过程的纵向跟踪误差

    Fig.  10  The longitudinal tracking error during the entire platoon lane-changing process

    图  11  性能过渡函数的示意图

    Fig.  11  Diagram of performance transition function

    图  12  虚拟恢复函数的示意图

    Fig.  12  Diagram of virtual recovery function

    图  13  情况1中队列的变道轨迹

    Fig.  13  Lane-changing trajectories of platoon in Case 1

    图  14  情况1中队列的跟踪误差

    Fig.  14  The tracking errors of platoon in Case 1

    图  15  情况1中队列的位移

    Fig.  15  The displacements of platoon in Case 1

    图  16  情况1中队列的控制输入

    Fig.  16  The control inputs of platoon in Case 1

    图  17  情况2中队列的变道轨迹

    Fig.  17  Lane-changing trajectories of platoon in Case 2

    图  18  情况2中队列的跟踪误差

    Fig.  18  The tracking errors of platoon in Case 2

    图  19  情况2中队列的位移

    Fig.  19  The displacements of platoon in Case 2

    图  20  情况2中队列的控制输入

    Fig.  20  The control inputs of platoon in Case 2

    图  21  文献[12]所提方法仿真结果

    Fig.  21  Simulation results under the method in reference [12]

    表  1  包络映射的一些示例

    Table  1  Some examples of envelope mapping

    类型 $ \Psi(\Phi_{pz_i}) $ $ \dfrac{\partial \Psi(\Phi_{pz_i})}{\partial\Phi_{pz_i}} $ $ \dfrac{\partial^2 \Psi(\Phi_{pz_i})}{\partial\Phi_{pz_i}^2} $
    1 $ {{\rm{ln}}}\left(\dfrac{1+\Phi_{pz_i}}{1-\Phi_{pz_i}}\right) $ $ \dfrac{2}{1-\Phi_{pz_i}^2} $ $ \dfrac{4\Phi_{pz_i}}{\left(1-\Phi_{pz_i}^2\right)^2} $
    2 $ {{\rm{tan}}}\left(\dfrac{\pi}{2}\Phi_{pz_i}\right) $ $ \dfrac{\pi\sec^2\left(\dfrac{\pi}{2}\Phi_{pz_i}\right)}{2} $ $ \dfrac{\pi^2{{\rm{sin}}}\left(\dfrac{\pi}{2}\varPhi_{pz_i}\right)}{2{{\rm{cos}}}^3\left(\dfrac{\pi}{2}\Phi_{pz_i}\right)} $
    3 $ \dfrac{2\Phi_{pz_i}}{\left(1+\Phi_{pz_i}\right)\left(1-\Phi_{pz_i}\right)} $ $ \dfrac{2+2\Phi_{pz_i}^2}{\left(1-\Phi_{pz_i}^2\right)^2} $ $ \dfrac{12\Phi_{pz_i}+4\Phi_{pz_i}^3}{\left(1-\Phi_{pz_i}^2\right)^3} $
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