[1]詹国立,陈龙淼.卧式链传动药仓的RBF网络自适应鲁棒滑模控制[J].机械与电子,2017,(11):41-46.
　ZHAN Guoli,CHEN Longmiao.Control of RBF Network Adaptive Robust Sliding Mode for Horizontal Chain Drive Gunpowder Storage Mechanism[J].Machinery & Electronics,2017,(11):41-46.
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卧式链传动药仓的RBF网络自适应鲁棒滑模控制
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2017年11期
页码:
41-46
栏目:
自动控制与检测
出版日期:
2017-11-25

文章信息/Info

Title:
Control of RBF Network Adaptive Robust Sliding Mode for Horizontal Chain Drive Gunpowder Storage Mechanism
文章编号:
1001-2257(2017)11-0041-06
作者:
詹国立; 陈龙淼
(南京理工大学机械工程学院,江苏 南京 210094)
Author(s):
ZHAN Guoli;  CHEN Longmiao
(School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094,China)
关键词:
多边形效应;  滑模控制;  自适应算法;  RBF神经网络
Keywords:
polygon effect;  sliding mode control;  adaptive algorithm;  RBF neural network
分类号:
TP273
文献标志码:
A
摘要:
卧式链传动药仓的转动惯量与阻尼系数会随着药仓转动与药块数量变化而变化,链传动引起的多边形效应会影响运动的定位精度。采用RBF神经网络和自适应方法对重要未知非线性参数进行估计,并选用等效期望轨迹的方式从控制上补偿多边形效应的影响; 通过设置合适的神经网络参数与自适应项,估计位置参数的变化趋势; 通过引入鲁棒项,保证了滑模控制策略的稳定性。采用合适的Lyapunov方程分析,从理论上证明算法的可行性,能够使系统在有限时间内收敛。通过仿真分析,验证了RBF神经网络与自适应参数具有较强的学习能力,证明了该算法能够保证药仓模型的运动精度,提高系统稳定性,削弱系统抖振。
Abstract:
The moment of inertia and the damping coefficient of the horizontal chain drive gunpowder storage mechanism change with the rotation of the system and the changes of the number of gunpowder unit. The accuracy of kinematic positioning is also affected by the polygon effect caused by chain transmission. The RBF neural network and adaptive algorithm was used to estimate the important unknown nonlinear parameters, and the equivalent expectation trajectory method was adopted to compensate the influence of polygon effect; By setting appropriate neural network parameters and adaptive terms, the change trend of position parameters was estimated; By introducing the robust terms, the stability of the sliding control strategy was guaranteed. With the appropriate equation analysis of Lyapunov, the feasibility of the algorithm was proved theoretically, which could converge the system for a limited time. Through simulation analysis, it has proved that the RBF neural network and the adaptive parameters have strong learning ability, and the proposed algorithm can guarantee the kinematic accuracy of the system, improve the stability, and reduce the chattering of the system.

参考文献/References:

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　HAN Shixia,GAO Song,CHEN Chaobo.Maximum Power Point Tracking for Photovoltaic System Based on Sliding Mode Control[J].Machinery & Electronics,2018,(11):65.

备注/Memo

备注/Memo:
收稿日期:2017-08-18
作者简介:詹国立(1992-),男,福建三明人,硕士研究生,研究方向为机械系统自动控制; 陈龙淼(1979-),男,湖南长沙人,教授,博士研究生导师,研究方向为兵器科学与技术。
更新日期/Last Update: 2017-11-25