| 段捷,陈禧龙,杨宏杰,赵河明,赵韬硕,丁明军,孔德景,于静.基于RBF神经网络的油气悬架平顺性研究[J].装备环境工程,2024,21(3):113-120. DUAN Jie,CHEN Xilong,YANG Hongjie,ZHAO Heming,ZHAO Taoshuo,DING Mingjun,KONG Dejing,YU Jing.Ride Comfort of Hydro-pneumatic Suspension Based on RBF Neural Network[J].Equipment Environmental Engineering,2024,21(3):113-120. |
| 基于RBF神经网络的油气悬架平顺性研究 |
| Ride Comfort of Hydro-pneumatic Suspension Based on RBF Neural Network |
| 投稿时间：2024-01-26 修订日期：2024-02-26 |
| DOI：10.7643/issn.1672-9242.2024.03.015 |
| 中文关键词: 油气弹簧 自主调节 动态整定 RBF-PID控制器 快速稳定 可靠控制中图分类号:U463.3 文献标志码:A 文章编号:1672-9242(2024)03-0113-08 |
| 英文关键词:hydro-pneumatic spring autonomic conditioning dynamically adjusted RBF-PID controller reach stable state quickly reliable control |
| 基金项目:国防科工局技术基础项目(JSZL2020208B002) |
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| Author | Institution |
| DUAN Jie | College of Mechatronic Engineering, North University of China, Taiyuan 030051, China |
| CHEN Xilong | College of Mechatronic Engineering, North University of China, Taiyuan 030051, China |
| YANG Hongjie | College of Mechatronic Engineering, North University of China, Taiyuan 030051, China |
| ZHAO Heming | College of Mechatronic Engineering, North University of China, Taiyuan 030051, China |
| ZHAO Taoshuo | China North Vehicle Research Institute, Beijing 100072, China |
| DING Mingjun | Huaihai Industries Group Co., Ltd., Shanxi Changzhi 046012, China |
| KONG Dejing | Shipbuilding Information Centre of China, Beijing 100101, China |
| YU Jing | Shipbuilding Information Centre of China, Beijing 100101, China |
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| 中文摘要: |
| 目的 提高整车的行驶性能，使车辆可以自主调节，以适应各种不同的路面，从而达到良好的缓冲效果。方法 以双气室油气弹簧为研究对象，对其组成和工作原理进行详细分析，推导出油气弹簧的阻尼力和刚度等非线性特性与车身位移的关系。根据RBF神经网络控制原理，设计RBF-PID控制器，依靠神经网络自学习性对PID参数动态整定，使整个车身振动衰减，快速达到稳定状态，并基于Matlab/Simulink平台建立仿真模型，在B级和C级不平路面输入的情况下，重点对轮胎动载荷、车身质心加速度以及油气悬挂动挠度等3项性能进行仿真和分析。结果 同普通PID控制相比，RBF-PID控制下，车身3项性能的RMS分别降低26%、54%、0.1%。结论 RBF-PID控制能够克服环境影响，实现油气弹簧特性的可靠控制，提高了车辆行驶的平顺性以及稳定性。 |
| 英文摘要: |
| The work aims to improve the driving performance of the whole vehicle so that the vehicle can autonomic conditioning to adapt to a variety of different road surfaces to achieve a good buffer effect. The composition and working principle and relationship between the damping force and stiffness of hydro-pneumatic spring and the displacement of vehicle body was deduced in detail. According to the principle of RBF neural network control, a RBF-PID controller was designed, and the PID parameters were dynamically adjusted by the self-learning habit of neural network, so that the vibration of the whole body attenuated and reached a stable state quickly. A simulation model was established based on Matlab/Simulink platform. With the input of Class B and Class C uneven road surface, the three performances of tire dynamic load, body centroids acceleration and dynamic deflection of oil-gas suspension were simulated and analyzed. Compared with the ordinary PID control, the RMSE of three performance of the body under RBF-PID control was reduced by 26%, 54% and 0.3% respectively. RBF-PID control can overcome the impact of the environment, realize the reliable control of the characteristics of the oil-gas spring, and improve the ride comfort and stability of the vehicle. |
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