|本期目录/Table of Contents|

[1]刘治钢,王军政,赵江波.永磁同步电机神经网络自适应滑模控制器设计[J].电机与控制学报,2009,(02):290-295.
 LIU Zhi-gang,WANG Jun-zheng,ZHAO Jiang-bo.Neural network adaptive sliding mode control for permanent magnet synchronous motor[J].,2009,(02):290-295.
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
期数:
2009年02
页码:
290-295
栏目:
出版日期:
2009-03-15

文章信息/Info

Title:
Neural network adaptive sliding mode control for permanent magnet synchronous motor
作者:
刘治钢; 王军政; 赵江波
北京理工大学自动化学院复杂系统的智能控制与决策实验室
Author(s):
LIU Zhi-gang; WANG Jun-zheng; ZHAO Jiang-bo
关键词:
永磁同步电机 RBF神经网络 滑模控制器 参数摄动 负载扰动
Keywords:
permanent magnet synchronous motors RBF neural network sliding mode control parameter variation load disturbance
分类号:
TP273
DOI:
-
文献标志码:
A
摘要:
设计了神经网络自适应滑模控制器。用RBF神经网络自动调整滑模控制器的切换项增益,无需建立包含参数摄动和干扰在内的整个系统的精确数学模型,有效提高了系统的稳定性和鲁棒性。采用Lyapunov稳定性理论证明了系统稳定性,并针对常值干扰、时变干扰和参数摄动情况分别进行了仿真与实验。与传统的PI控制相比,神经网络自适应滑模控制器具有更好的稳定性和抗干扰能力。
Abstract:
Considering the sensitivity to parameter variation and load disturbance of Permanent magnet synchronous motor(PMSM),this paper proposed a neural network based adaptive sliding mode control(NNASMC) for higher stability and robustness.RBF neural network was used to adjust the gain of the switch part of sliding mode control input.So the accurate mathematic model of the whole system including uncertain parameters and disturbance was not required.The stability of the system was proved by Lyapunov theory.Simulations and experiments are done under the situation of constant disturbance,time-varing disturbance and parameter variation.The proposed NNASMC has a better stability and noise reduction compared with PI control.

参考文献/References:

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备注/Memo

备注/Memo:
“985”工程学科建设投资项目(107008200400020)
更新日期/Last Update: 2009-07-08