摘要
应用经典摩擦模型描述液压缸摩擦力时,由于未考虑油液压力效应对摩擦力的影响,模型预测效果欠佳。为了克服该问题,引入压力影响系数和动态摩擦时间常数,基于Stribeck和广义Maxwell滑动模型(GMS)提出了改进的稳态摩擦模型(P-Stribeck)和动态摩擦模型(PGMS)。搭建了伺服阀控液压缸系统摩擦特性测试实验台,在不同密封形式、不同缸径、不同负载、不同加速度及频率下进行了液压缸往复运动摩擦特性测试。采用智能遗传算法,利用液压缸测试实验台采集的进出口压力、位移、速度、摩擦力等数据,分别采用改进的稳态摩擦模型和动态摩擦模型进行参数辨识和模型检验。对不同复杂工况下实验数据与经典摩擦模型以及所提出的改进模型的预测结果进行对比和误差分析,结果表明:P-Stribeck模型预测液压缸稳态摩擦力的精度明显优于Stribeck模型,P-GMS模型预测液压缸动态摩擦力的精度优于GMS摩擦模型,从而验证了所提出摩擦模型的有效性。
The friction force cannot be predicted accurately by using the classical friction model without considering hydraulic fluid pressure effects.In order to solve this problem,the modified steady-state friction model(P-Stribeck)and dynamic friction model(PGMS)based on Stribeck and generalized Maxwell slip model(GMS)were proposed by introducing pressure influence coefficient dynamic friction time constant.To validate the effectiveness of those proposed friction models,a hydraulic cylinder test platform was developed to study the friction characteristics of hydraulic cylinders.Therefore,the friction behaviors of hydraulic cylinders for different seal types,different cylinder bores,different loads,different velocity and frequencies were investigated.The intelligent genetic algorithm was adopted to identify the friction parameters of the proposed steady friction model and dynamic friction model.The identification and validation procedures of the developed friction models were conducted with the data of inlet and outlet pressure,displacement/velocity,friction force and other data collected from the hydraulic cylinder test bench.The experimental results were compared with that of predicted by the classic friction models and proposed friction models.Also,an error analysis procedure of the proposed friction models was conducted under different operation conditions.The results showed that the predicting accuracy of steady state friction force of the P-Stribeck model was better than that of the Stribeck model;in addition,the precision of friction estimation of the proposed P-GMS model was higher than that of the GMS model.
作者
李毅波
曾云龙
潘晴
姜雪鹏
LI Yibo;ZENG Yunlong;PAN Qing;JIANG Xuepeng(Light Alloy Research Institute,Central South University,Changsha 410083,China;State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou 310027,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第11期418-426,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(51805551)
湖南省自然科学基金项目(2019JJ50799)
流体动力与机电系统国家重点实验室开放基金课题项目(GZKF-201924)
中南大学研究生自主探索创新项目(2019zzts524)。
