分布式电动汽车急转弯路面下的合作博弈鲁棒协调控制。

IF 6.5
Wei Li, Chunyan Wang, Wanzhong Zhao, Linfeng Lv, Jiabing Gao
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引用次数: 0

摘要

四轮转向(4WS)和四轮独立驱动(4WID)作为分布式电动汽车(DEV)的关键技术,能够有效提高车辆的路径跟踪精度和横向稳定性。然而,在急转弯路面下,轮胎非线性和纵横耦合效应显著加剧,增加了动力学建模的复杂性。同时,4WS和4WID之间的控制目标冲突以及时变速度等干扰会降低精确路径跟踪时的横向稳定性。为了解决这些问题,我们提出了一种基于Takagi-Sugeno (T-S)模糊的合作博弈鲁棒协调控制器(CGRCC)。首先,基于T-S模糊建立了考虑轮胎非线性和纵横耦合效应的车路离散误差模型,准确捕捉了单个轮胎的动态特性,提高了DEV建模精度;其次,基于4WS和4WID之间的动态交互,设计了协同博弈协调控制器,实现多作动器协同优化,以协调精确路径跟踪与横向稳定性之间的目标耦合冲突。最后,设计了一种基于模糊lyapunov的H∞干扰抑制器,以减轻包括时变车速在内的干扰对系统性能的影响。实验结果表明,在大曲率双线变化情况下,CGRCC与对照控制器相比,路径跟踪误差分别降低33.3% %、48.1 %和60.3 %,横向速度误差分别降低15.5 %、16.3 %和24.6 %,显示了CGRCC在急转弯条件下的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooperative game robust coordination control for distributed electric vehicle under sharply turning roads.

As key technologies for distributed electric vehicle (DEV), four-wheel steering (4WS) and four-wheel independent drive (4WID) can effectively enhance the path-tracking accuracy and lateral stability. However, when under a sharply turning road, tire nonlinearity and longitudinal-lateral coupling effects are significantly exacerbated, leading to increased complexity in dynamic modeling. Meanwhile, control objective conflicts between 4WS and 4WID, as well as disturbances including time-varying speed, may reduce lateral stability during precise path-tracking. To address these challenges, we propose a cooperative game robust coordination controller (CGRCC) based on Takagi-Sugeno (T-S) fuzzy. First, a vehicle-road discrete error model considering tire nonlinearity and longitudinal-lateral coupling effects is established based on T-S fuzzy, which precisely captures the dynamic characteristics of individual tires and enhances DEV modeling accuracy. Second, a cooperative game coordination controller is designed based on the dynamic interaction between 4WS and 4WID, achieving multi-actuator collaborative optimization to harmonize the objective coupling conflicts between precise path-tracking and lateral stability. Finally, a fuzzy Lyapunov-based H disturbance suppressor is developed to mitigate the impact of the disturbances, including time-varying vehicle speed, on system performance. The experimental results show that under large curvature double line changes, CGRCC reduces the path-tracking error by 33.3 %, 48.1 % and 60.3 %, and the lateral speed error by 15.5 %, 16.3 % and 24.6 %, respectively, compared with the comparison controllers, demonstrating the efficacy and preeminence of CGRCC under sharply turning roads.

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