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主动平衡减速器实验系统辨识研究
Identification Research of Active Balancing Reducer Experimental System
【作者】 郑凯;
【导师】 张英;
【作者基本信息】 北京交通大学 , 机械设计及理论, 2019, 硕士
【摘要】 主动平衡减速器是一种集主动平衡和减速功能于一体的新型动力性能柔性调节装置,主动平衡减速器实验系统是为了验证主动平衡减速器动力性能而搭建的。构建系统精确的数学模型是系统动力性能分析的基础。在使用动力学方法所建系统理论模型对主动平衡减速器实验系统进行仿真分析和实验研究时发现,两者的结果存在一定的偏差。系统辨识是根据已知或测得的实验数据构建系统数学模型的方法。为了获得与实际系统更加吻合的系统模型,本文采用系统辨识方法对实验系统进行了研究。本文采用神经网络系统辨识方法构建主动平衡减速器实验系统的数学模型。基于系统理论模型和辨识模型对主动平衡减速器实验系统进行动力性能分析,仿真和实验结果表明,采用系统辨识方法构建的系统辨识模型与实际系统更加吻合,将此模型用于系统动力性能分析使系统的动力性能得到了明显的提高。具体工作内容包括:(1)基于实验室现有实验设备,搭建了主动平衡减速器实验系统的数据采集系统;设计了实验方案,并完成了不同实验条件下的实验和数据采集,获得了系统辨识所需的原始数据。(2)结合实验数据特点,提出了将消除趋势项、小波阈值去噪和基于低频滤波的频域积分三种方法结合的数据处理方法,对实验数据进行处理,得到了满足要求的系统振动响应数据。(3)根据实验系统的非线性特征,对几种BP神经网络算法及其辨识效果进行了仿真分析,选定其中辨识效果最好的Levenberg-Marquardt算法对主动平衡减速器实验系统进行了辨识,得到了系统辨识模型。(4)构建了主动平衡减速器实验系统的理论模型,并基于系统理论模型和辨识模型进行系统动力性能的仿真和实验研究,证明了系统辨识模型更加接近真实系统,同时还表明,基于系统辨识模型对主动平衡减速器控制参数进行优化设计,可以获得对系统动力性能更好的调节效果。
【Abstract】 Active balancing reducer(ABR)is a new flexible adjusting device for dynamic performance,which integrates the functions of active balancing and speed reducing.The experimental system was built to verify the dynamic performance of ABR.The precise mathematical model is very important for the dynamic performance analysis.There existed some deviations between the results of the simulation and experiments while using the theoretical model of the system.System identification is a method to set up system mathematical model based on measured experimental data.In order to obtain a more precise system model of the test bed,the system identification method was used in this paper.Neural network system identification method was seleced to set up the identification model of the ABR experimental system based on an amount of data.The dynamic performance of the ABR experimental system was analyzed based on the system theory model and identification model through simulation and experimental method,respectively.The results showed that the identification model was more accurate to the actual system,and the dynamic performance of the system based on the identification model was improved.Specific work includes:(1)The data collection system of the ABR experimental system was set up based on the existing test bed.The experimental scheme was designed for further study of the system identification.The experimental data under different experimental conditions was collected and saved.(2)A kind of data processing method was proposed according to the characteristics of experimental data,which consisted of elimination of trend terms,wavelet threshold denoising and frequency domain integration based on low-frequency filtering.The experimental data were processed by the proposed method and the qualified system vibration response data were obtained.(3)On account of the non-linear characteristics of the experimental system,several algorithms based on BP neural network were studied and the identification effects by using those algorithms were analyzed through simulation.Among them,Levenberg-Marquardt algorithm had the best identification effect.The identification process was carried out by using the selected Levenberg-Marquardt algorithm and the model of the ABR experimental system was obtained.(4)The theoretical model of the ABR experimental system was deduced.The simulation and experimental study on the vibration respond of the system was carried out based on the theoretical model and the identification model,respectively.The results showed the identification model of the system was more approximate to the real system.At the same time,it was shown that the optimal design of the control parameters of the ABR based on the identification model had better balancing effect on the dynamic performance of the system.
【Key words】 System identification; Active Balancing Reducer; Data processing; Wavelet denoising; BP neural network identification; Vibration response;
- 【网络出版投稿人】 北京交通大学 【网络出版年期】2020年 01期
- 【分类号】TH132.46
- 【被引频次】1
- 【下载频次】86