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含惯容器的仿生X形悬架设计与减振性能分析 被引量:1
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作者 杨所 韩世昌 李茜 《兵器装备工程学报》 CAS CSCD 北大核心 2024年第6期260-266,共7页
针对履带车辆悬架减振性能优化的问题,将滚珠丝杠惯容器与仿生X形结构结合,设计了一种含惯容器的仿生X形履带车辆悬架。首先,以悬架单体为研究对象,以悬架位移传递率为性能指标,进行振动分析,结果表明,相较于传统弹簧-阻尼悬架和仿生X... 针对履带车辆悬架减振性能优化的问题,将滚珠丝杠惯容器与仿生X形结构结合,设计了一种含惯容器的仿生X形履带车辆悬架。首先,以悬架单体为研究对象,以悬架位移传递率为性能指标,进行振动分析,结果表明,相较于传统弹簧-阻尼悬架和仿生X形悬架,加入惯容器后,悬架的性能更好、主频更低,且当输入激励在5~11 Hz范围内时,系统的位移传递率最低,减振效果最好。其次,建立了一组履带车辆半车模型,以3种不同等级的随机路面信号作为激励,对车辆的点头加速度进行分析,对比传统仿生X形悬架,含惯容器悬架的点头加速度分别降低了9.3%、42.98%以及65.91%,且路面等级越高,路况越复杂,悬架点头加速度越低,减振效果越好。研究结果为利用惯容器和仿生X形结构,设计减振效果更好的新型履带车辆悬架提供了参考。 展开更多
关键词 履带车辆悬架 设计 惯容器 减振分析
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Neuron PI control for semi-active suspension system of tracked vehicle 被引量:3
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作者 曾谊晖 刘少军 鄂加强 《Journal of Central South University》 SCIE EI CAS 2011年第2期444-450,共7页
A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An... A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%. 展开更多
关键词 tracked vehicle magneto rheological damper semi-active suspension preview technology neuron PI control
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