基于双目距离识别的智能悬架多模式切换控制研究

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC World Electric Vehicle Journal Pub Date : 2023-12-07 DOI:10.3390/wevj14120340
Chen Huang, Kunyan Lv, Qing Xu, Yifan Dai
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引用次数: 0

摘要

随着人们对汽车驾驶舒适性要求的提升,传统的汽车被动悬架由于其阻尼和刚度的不可调性,已经不能满足现有的需求,因此半主动悬架和主动悬架越来越被人们所接受。与主动悬架相比,半主动悬架具有制造成本低、结构可靠等优点,已成为大多数车辆的首选。为了优化半主动悬架在不同工况下的控制效果,本文完成了磁流变半主动悬架系统动力学和路面输入的建模;然后,设计双目摄像机感知算法,利用点云测距函数获取目标的实时距离,并获得悬浮控制所需的参数。随后完成了控制模式切换规则和悬架控制器的设计。根据控制目标的不同,该模式可分为障碍模式、直线模式和曲线模式。悬架控制器包括BP-PID(神经网络PID控制器)控制器和分配器。最后,通过系统仿真和硬件在环测试验证了模式切换规则和控制方法的有效性。
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Research on the Multimode Switching Control of Intelligent Suspension Based on Binocular Distance Recognition
As the upgrade of people’s requirements for automotive driving comfort, conventional passive suspensions for cars have fallen short of existing demands due to their nonadjustable damping and stiffness, so semiactive suspensions and active suspensions have gained growing acceptance. Compared with active suspensions, semiactive suspensions offer the advantages of a low manufacturing cost and reliable structure, and thus have become the preferred choice for most vehicles. To optimize the control effect of semiactive suspensions under different working conditions, this paper completed the modeling of magnetorheological semiactive suspension system dynamics and road inputs; then, the design of binocular camera sensing algorithms was performed to obtain the real-time distance of the target using the point cloud ranging function, and the parameters required for suspension control were also obtained. This was followed by the completion of the control-mode-switching rules and the design of the suspension controller. According to the different control objectives, the mode could be divided into the obstacle-road mode, straight-road mode, and curved-road mode. The suspension controller included the BP-PID (neural network PID controller) controller and the force distributor. Finally, the effectiveness of the mode-switching rules and the control method was verified through system simulation and the hardware-in-the-loop test.
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来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
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