[1]邵辉,胡艳丽,洪雪梅,等.神经网络辨识的液压挖掘机LPV模型[J].华侨大学学报(自然科学版),2016,37(1):43-47.[doi:10.11830/ISSN.1000-5013.2016.01.0043]
 SHAO Hui,HU Yanli,HONG Xuemei,et al.LPV Model of Hydraulic Excavator Based on Neural Network Identification[J].Journal of Huaqiao University(Natural Science),2016,37(1):43-47.[doi:10.11830/ISSN.1000-5013.2016.01.0043]
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神经网络辨识的液压挖掘机LPV模型()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第37卷
期数:
2016年第1期
页码:
43-47
栏目:
出版日期:
2016-01-03

文章信息/Info

Title:
LPV Model of Hydraulic Excavator Based on Neural Network Identification
文章编号:
1000-5013(2016)01-0043-05
作者:
邵辉 胡艳丽 洪雪梅 王飞
华侨大学 信息科学与工程学院, 福建 厦门 361021
Author(s):
SHAO Hui HU Yanli HONG Xuemei WANG Fei
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
液压挖掘机 动臂关节 神经网络 线性变参数 辨识
Keywords:
hydraulic excavator boom joint neural network variable linear parameter identification
分类号:
TP273
DOI:
10.11830/ISSN.1000-5013.2016.01.0043
文献标志码:
A
摘要:
针对液压挖掘机动臂关节的非线性建模问题,提出一种基于神经网络的线性变参数(LPV)模型的辨识方法.在各个工作点处根据其关节速度的一阶惯性加延迟模型,获得其关节角度模型;结合调度变量特性,采用神经网络辨识出LPV模型的参数,设计出挖掘机动臂在全局工作范围的LPV模型.通过仿真实验,验证了该方法的有效性和模型的准确性.
Abstract:
A linear parameter varying model is proposed based on neural network identification for building the hydraulic excavator boom model. The model of the joint angle is obtained based on the first-order plus dead time model of the joint velocity at each working-point. Depending on scheduling variable characteristics, the LPV model parameters are identified by using neural network, and the global LPV model of the excavator boom in the workspace is designed. The simulations and experiments indicate the accuracy of the model and the validity of the method.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2014-09-25
通信作者: 邵辉(1973-),女,副教授,博士,主要从事机器人控制、运动规划、过程控制及智能控制的研究.E-mail:shaohuihu11@163.com.
基金项目: 国家自然科学青年基金计划资助项目(61203040); 福建省科技计划引导性项目(Z1525022); 福建省泉州市科技计划项目(2013Z34)
更新日期/Last Update: 2016-01-20