摘要
在数控液压伺服激振试验台上进行滚珠丝杠式惯容器力学性能试验，获得惯容器在不同惯容系数及不同激振输入下力学响应，通过分析惯容器存在的非线性因素及试验结果，揭示非线性因素影响惯容器的实际性能。考虑建立惯容器自适应神经网络模型，进行惯容器力学性能预测。由于BP算法易陷入局部最优且泛化能力弱，用遗传算法优化BP网络训练过程。基于非线性因素对惯容器力学性能影响机理，选惯容系数及惯容器在多个瞬态时间点位移、速度及加速度为神经网络输入，惯容器输出力为网络输出，并将试验所得1020组数据用于网络训练及预测，网络预测结果与试验结果吻合良好，说明所用方法正确合理，可为惯容器力学性能预测提供参考。
Abstract
This paper presents the experimental results from the testing of ball-screw inerter with different inertances and different excitation inputs which were carried out using a CNC hydraulic servo exciting test-platform. The experimental results show that the dynamic characteristics of inerter are influenced by the nonlinear factors. In order to master the dynamic performance of inerter, a neural network for mechanical properties prediction was considered to be constructed. To the problem that the BP algorithm is prone to fall into local optimum, the genetic algorithm was used to optimize the training process and improve the generalization ability of the neural network. According to the influencing mechanism of the nonlinear factors on the mechanical properties of inerter, the input variables of neural network were determined to be the inertance and the displacement, velocity and acceleration of inerter under multiple transient time points. 1020 groups of test data were used for network training and prediction and the prediction results are identical to the test results. It is shown that this method can be used as a mechanical properties prediction method for inerter.
关键词
滚珠丝杠式惯容器 /
试验 /
非线性 /
遗传BP神经网络 /
性能预测
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Key words
ball-screw inerter /
experiment /
nonlinearity /
GA-BP neural network /
performance prediction
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孙晓强;陈 龙;汪若尘;张孝良;陈月霞.
滚珠丝杠式惯容器试验及力学性能预测[J]. 振动与冲击, 2014, 33(14): 61-65
SUN Xiao-qiang;CHEN Long;WANG Ruo-chen;ZHANG Xiao-liang;CHEN Yue-xia.
Experiment and mechanical properties prediction of ball-screw inerter[J]. Journal of Vibration and Shock, 2014, 33(14): 61-65
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脚注
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