The dynamic performance of a nozzle-flapper servo valve can be affected by several factors such as the disturbance of the input signal,the motion of the armature assembly and the oscillation of the jet force.As the pa...The dynamic performance of a nozzle-flapper servo valve can be affected by several factors such as the disturbance of the input signal,the motion of the armature assembly and the oscillation of the jet force.As the part of vibrating at high frequency,the armature assembly plays a vital role during the operation of the servo valve.In order to accurately predict the transient response of the armature assembly during the vibration,a mathematical model of armature assembly is established based on the distributed parameters method(DPM)and Hamilton principle.The new mathematical model is composed of three main parts,the modal eigenfunction,modal mechanical response expressions of the spring tube and the motion equation of the other armature assembly.After programing,the purpose of using the DPM to predict the dynamic response of different positions located on the armature assembly is achieved.For verifying the validity of the mathematical model,the finite element method(FEM)and classic model(CM)of armature assembly are applicated by commercial software under the same condition.The comparison results prove that the DPM can effectively predict the axial and tangential deflection of the armature assembly different positions which the CM can’t duing to its over-simplification.A certain error is generated when predicting the axial deformation at different heights by DPM,which is caused by an approximate method to simulate the torsion of the spring tube.The comparison results of the spring tube deflection at different vibration frequencies shows that the adaptability of DPM is significantly higher than the classic model,which verify the model is more adaptable for predicting the dynamic response of the armature assembly.展开更多
【目的】针对机器人自主装配研究多聚焦静态目标,移动目标装配相关研究不足的问题,提出一种视觉阻抗控制器的变参数控制方法,以提升机器人在动态环境中目标跟踪与移动装配的适应性和柔顺性。【方法】首先,结合力反馈与视觉反馈信息,在...【目的】针对机器人自主装配研究多聚焦静态目标,移动目标装配相关研究不足的问题,提出一种视觉阻抗控制器的变参数控制方法,以提升机器人在动态环境中目标跟踪与移动装配的适应性和柔顺性。【方法】首先,结合力反馈与视觉反馈信息,在特征空间构建2阶阻抗控制器,为动态装配提供控制基础;其次,将蒙特卡洛随机失活(Monte Carlo Dropout,MCD)作为概率动力学模型,改进概率推理学习控制优化(Probabilistic Inference for Learning Control Optimization,PILCO)算法,平衡状态不确定性推理能力与计算效率;最后,以特征误差和机器人关节位置信息为观测空间,自适应调整控制器阻抗参数,优化动态跟踪与装配性能。【结果】仿真及试验台验证结果表明,相比原高斯过程模型,MCD模型在保留状态不确定性推理能力的同时,显著缩短训练时间(50轮训练时间从43.50 h降至12.82 h);装配成功率从94.5%提升至98.0%,平均装配用时从5.820 s缩短至3.253 s,超调量大幅减少,跟踪响应更及时。研究可为移动目标柔顺装配提供参考。展开更多
基金supported by National Natural Science Foundation of China(No.51675119)。
文摘The dynamic performance of a nozzle-flapper servo valve can be affected by several factors such as the disturbance of the input signal,the motion of the armature assembly and the oscillation of the jet force.As the part of vibrating at high frequency,the armature assembly plays a vital role during the operation of the servo valve.In order to accurately predict the transient response of the armature assembly during the vibration,a mathematical model of armature assembly is established based on the distributed parameters method(DPM)and Hamilton principle.The new mathematical model is composed of three main parts,the modal eigenfunction,modal mechanical response expressions of the spring tube and the motion equation of the other armature assembly.After programing,the purpose of using the DPM to predict the dynamic response of different positions located on the armature assembly is achieved.For verifying the validity of the mathematical model,the finite element method(FEM)and classic model(CM)of armature assembly are applicated by commercial software under the same condition.The comparison results prove that the DPM can effectively predict the axial and tangential deflection of the armature assembly different positions which the CM can’t duing to its over-simplification.A certain error is generated when predicting the axial deformation at different heights by DPM,which is caused by an approximate method to simulate the torsion of the spring tube.The comparison results of the spring tube deflection at different vibration frequencies shows that the adaptability of DPM is significantly higher than the classic model,which verify the model is more adaptable for predicting the dynamic response of the armature assembly.
文摘【目的】针对机器人自主装配研究多聚焦静态目标,移动目标装配相关研究不足的问题,提出一种视觉阻抗控制器的变参数控制方法,以提升机器人在动态环境中目标跟踪与移动装配的适应性和柔顺性。【方法】首先,结合力反馈与视觉反馈信息,在特征空间构建2阶阻抗控制器,为动态装配提供控制基础;其次,将蒙特卡洛随机失活(Monte Carlo Dropout,MCD)作为概率动力学模型,改进概率推理学习控制优化(Probabilistic Inference for Learning Control Optimization,PILCO)算法,平衡状态不确定性推理能力与计算效率;最后,以特征误差和机器人关节位置信息为观测空间,自适应调整控制器阻抗参数,优化动态跟踪与装配性能。【结果】仿真及试验台验证结果表明,相比原高斯过程模型,MCD模型在保留状态不确定性推理能力的同时,显著缩短训练时间(50轮训练时间从43.50 h降至12.82 h);装配成功率从94.5%提升至98.0%,平均装配用时从5.820 s缩短至3.253 s,超调量大幅减少,跟踪响应更及时。研究可为移动目标柔顺装配提供参考。
