基于神经网络的薄壳多目标振动优化控制研究

doi:10.3901/JME.2016.09.056

机械工程学报 ›› 2016, Vol. 52 ›› Issue (9): 56-64.doi: 10.3901/JME.2016.09.056

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基于神经网络的薄壳多目标振动优化控制研究

张兴武^{1, 2}, 刘金鑫^{1, 2}, 陈雪峰^{1, 2}, 曹宏瑞^{1, 2}

1. 西安交通大学机械工程学院西安 710049;
2. 西安交通大学机械制造系统工程国家重点实验室西安 710054

出版日期:2016-05-05 发布日期:2016-05-05
作者简介:张兴武,男,1984年出生,博士,讲师,硕士研究生导师。主要研究方向为小波有限元动力学分析与振动优化控制。E-mail：xwzhang@mail.xjtu.edu.cn;刘金鑫,男,1988年出生,博士研究生。主要研究方向为振动优化控制。E-mail：liujin_xin@stu.xjtu.edu.cn;陈雪峰(通信作者),男,1975年出生,博士,教授,博士研究生导师。主要研究方向为有限元动态分析与数字化制造,机械故障诊断、安全监测与寿命预测,重大装备的振动优化控制。E-mail：chenxf@mail.xjtu.edu.cn;曹宏瑞,男,1982年出生,博士,副教授,博士研究生导师。主要研究方向为主轴动力学建模与振动控制。E-mail：chr@mail.xjtu.edu.cn
基金资助:
国家自然科学基金(51405370, 51225501)、国家重点基础研究发展计划 (973计划,2015CB057400)、陕西省自然科学基金(2015JQ5184)和陕西省博士后基金资助项目

Neural Network Based Multi-objective Active Vibration Optimization Method for Shell Structure

ZHANG Xingwu, LIU Jinxin, CHEN Xuefeng, CAO Hongrui

1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049;
2. State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710054

Online:2016-05-05 Published:2016-05-05

摘要/Abstract

摘要： 针对目前主动控制方法主要集中于减振降噪方面的研究,无法满足工程中频率特性改变的需求等问题,结合神经网络的多目标并行处理能力,提出基于神经网络的多目标振动优化控制方法。首先,基于神经网络算法,构造频域主动控制架构,相较于时域方法,该架构一个控制循环只需一次傅里叶变换(Fast Fourier transform, FFT),无傅里叶逆变换(Inverse fast Fourier transform, IFFT),因此,控制时效性得到有效保证。其次,基于全局频域误差与特征频点误差,构造混合型误差评判准则,提升算法的自适应性,可靠性与抗干扰能力。再次,基于多自由度系统方程,研究了多目标控制中的可控性问题,保证控制的可行性。最后,通过大型薄壳结构的八点多目标振动优化控制,有效验证了方法的有效性与可行性。

关键词: 多目标, 混合误差准则, 频率特性, 神经网络, 振动优化

Abstract: Active control method mainly focuses on vibration and noise suspension at present thus can’t satisfy the requirement of frequency characteristics control. Therefore, based on the multi-objective parallel processing ability of neural network, the multi-objective vibration optimization method is proposed to deal with this problem. First, the frequency-domain control frame is constructed based on neural network algorithm. Compared with traditional time-domain methods, the proposed control frame just require once FFT in each iteration and no IFFT needed, so the control efficiency can be guaranteed. Second, hybrid error criterion is constructed by combining global frequency error and frequency node error together to improve the adaptability, reliability and anti-interference ability. Third, the controllability problem of the multi-objective method in implementation is studied through mathematical analysis. At last, the effectiveness of the proposed multi-objective method is verified through vibration optimization on eight points of shell structure.

Key words: frequency characteristic, hybrid error criterion, multi-objective, neural network, vibration optimization

中图分类号:

TB535

张兴武, 刘金鑫, 陈雪峰, 曹宏瑞. 基于神经网络的薄壳多目标振动优化控制研究[J]. 机械工程学报, 2016, 52(9): 56-64.

ZHANG Xingwu^{1, 2}, LIU Jinxin^{1, 2}, CHEN Xuefeng^{1, 2}, CAO Hongrui^{1, 2}. Neural Network Based Multi-objective Active Vibration Optimization Method for Shell Structure[J]. Journal of Mechanical Engineering, 2016, 52(9): 56-64.

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基于神经网络的薄壳多目标振动优化控制研究

Neural Network Based Multi-objective Active Vibration Optimization Method for Shell Structure

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