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磁力馈能主动悬架稳定性分析

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  • 国家科技期刊平台
  • NETL
  • NSTL
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为了提高车辆行驶平顺性和安全性,提出了一种新型磁力馈能主动悬架,并研究其稳定性.采用BP神经网络PID控制算法(BP-PID)搭建磁力馈能主动悬架控制系统,利用MATLAB/Simulink进行理论仿真.建立B级和C级随机路面,仿真分析不同速度、不同等级下,磁力馈能主动悬架的稳定性.结果表明,相比于被动悬架效果和PID控制的磁力馈能主动悬架效果,采用BP-PID控制可明显提高悬架稳定性.B级随机路面60 km/h时车身垂直加速度改善了45.90%,证明了BP-PID控制的合理性,可有效提升悬架稳定性.
Stability analysis of magnetic energy harvesting active suspension
In order to improve the ride comfort and safety of vehicles,a new magnetic energy harvesting active suspension was proposed and its stability was studied.BP neural network PID control algorithm(BP-PID)was used to build a magnetic energy harvesting active suspension control system,and theoretical simulation was conducted by using MATLAB/Simulink.B-level and C-level random pavements were established to simulate and analyze the stability of magnetic energy harvesting active suspension at different speeds and pavement levels.The result shows that BP-PID control significantly improves suspension stability,in comparison with the passive suspension and the magnetic energy harvesting active suspension under PID control.The vertical acceleration of the vehicle body gets improved by 45.90%at 60 km/h on B-level random road surfaces,demonstrating the rationality of BP-PID control,thus effectively improve the suspension stability.

magnetic energy harvesting active suspensionproportional integral differential controlback propagation neural networkpassive suspensionvertical acceleration of vehicle bodysuspension dynamic stroketire dynamic displacement

陈丽、许丽、周冉、孙凤

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沈阳工业大学人工智能学院,辽宁 沈阳 110870

沈阳工业大学机械工程学院,辽宁 沈阳 110870

磁力馈能主动悬架 比例积分微分控制 反向传播神经网络 被动悬架 车身垂直加速度 悬架动行程 轮胎动位移

国家自然科学基金国家自然科学基金辽宁省教育厅项目辽宁省重点实验室建设项目

5200534552005344LJGD20190112020JH6/10500048

2024

10.7688/j.issn.1000-1646.2024.03.09

沈阳工业大学学报
沈阳工业大学

沈阳工业大学学报

CSTPCD北大核心
影响因子：0.62
ISSN：1000-1646
年,卷(期)：2024.46(3)
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