|本期目录/Table of Contents|

[1]王 权,李 军,戴 立.基于BP神经网络的电动伺服加载算法研究[J].工业仪表与自动化装置,2017,(02):8-14.
　WANG Quan,LI Jun,DAI Li.Research on electric loading simulator algorithms based on BP neural network[J].Industrial Instrumentation & Automation,2017,(02):8-14.
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基于BP神经网络的电动伺服加载算法研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2017年02期
页码:
8-14
栏目:
出版日期:
2017-04-15

文章信息/Info

Title:
Research on electric loading simulator algorithms based on BP neural network
文章编号:
1000-0682(2017)02-0000-00
作者:
王 权; 李 军; 戴 立
（南京理工大学 自动化学院，南京 210094）
Author(s):
WANG Quan;  LI Jun;  DAI Li
(School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China)
关键词:
电动加载系统; BP神经网络; 多余力矩; 复合控制
Keywords:
electric loading system;  BP neural network;  surplus torque;  compound control
分类号:
TP302
DOI:
-
文献标志码:
A
摘要:
该文从加载控制器角度出发将BP神经网络算法引入加载系统削弱多余力矩对系统的影响，提高加载精度。建立了电动伺服加载系统的数学模型，分析了多余力矩的产生原因以及基于结构不变原理存在的局限性。介绍了BP神经网络控制算法基本原理，并给出了具体控制结构及相应算法，设计了一种BP/PID复合控制器。仿真结果表明，复合控制器有效地抑制了系统的多余力矩，降低跟踪误差，改善加载系统的动态性能，提高了跟踪精度，增强了稳定性。
Abstract:
This paper introduces the intelligent control algorithm of BP neural network into the loading system to weaken the influence of surplus torque on the system, and to improve the accuracy of loading.Firstly, the mathematical model of the electric servo loading system is established, and the reasons of the surplus torque and the limitation of the structure invariance principle are analyzed. Secondly, this paper introduces the basic principle of BP neural network control algorithm, and gives the concrete control structure and the corresponding algorithm, and designs a kind of BP/PID composite controller.The simulation result shows that the composite controller can not only effectively restrain the surplus torque of system, reduce tracking error and improve the dynamic performance of the loading system, but also improve the tracking precision and enhace the stability.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2016-06-27
作者简介：王权（1992），男，硕士研究生，主要研究方向为加载系统控制算法研究。
更新日期/Last Update: 1900-01-01