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基于非经典阻尼模态的车辆滚摆振动分析 被引量:1
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作者 关庆华 李昌隆 +3 位作者 张斌 李伟 温泽峰 梁树林 《中南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2023年第12期4956-4967,共12页
建立考虑车体和构架横移与侧滚自由度的车辆系统滚摆振动模型,利用复模态分解法推导系统响应公式;借助于相位同步模态分析揭示车体与构架的横移和侧滚响应与模态响应的物理关系。研究结果表明:车辆系统滚摆振动由4阶模态组成,分别为车... 建立考虑车体和构架横移与侧滚自由度的车辆系统滚摆振动模型,利用复模态分解法推导系统响应公式;借助于相位同步模态分析揭示车体与构架的横移和侧滚响应与模态响应的物理关系。研究结果表明:车辆系统滚摆振动由4阶模态组成,分别为车体和构架的同相下心滚摆、同相上心滚摆、反相下心滚摆以及反相上心滚摆;轴箱外置车辆各阶模态阻尼比均比轴箱内置车辆的阻尼比大;在频率为10 Hz以内时,轴箱外置车辆的车体和构架响应明显比轴箱内置车辆的响应低,当频率为10 Hz以上时,这2种车型相差较小;当频率低于2 Hz时,这2种车型的车体响应比构架的响应大;当频率高于2 Hz时,构架响应比车体的响应大;采用合理柔性的二系横向刚度以及优化二系横向减振器阻尼,对降低车体低频振动最为有效。 展开更多
关键词 铁道车辆 振动 滚摆 复模态分析 悬挂系统参数
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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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