Wavelet-based Elman neural network complementary sliding mode control for permanent magnet linear servo system

被引：0
|
作者：
Jin H.-Y. [1 ]
Zhao X.-M. [1 ]
机构：
[1] School of Electrical Engineering, Shenyang University of Technology, Shenyang
来源：
Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2019年 / 23卷 / 10期
关键词：
Complementary sliding mode control; Permanent magnet linear synchronous motor; Recurrent wavelet-based Elman neural network; Robust compensator; Uncertainties;
D O I：
10.15938/j.emc.2019.10.012
中图分类号：
学科分类号：
摘要：
Permanent magnet linear synchronous motor (PMLSM) direct drive servo system is susceptible to uncertainties such as parameter variations, external disturbances and frictions, and it reduces the control performance of the system. A complementary sliding mode control based on recurrent wavelet-based Elman neural network (RWENN) method is proposed to solve the problems. Firstly, a dynamic model of PMLSM with uncertainties was established. Then, complementary sliding mode controller with the combination of the integral sliding mode surface and the complementary sliding surface was designed. In order to solve the problem that the parameters of complementary sliding mode controller are difficult to be chosen, and estimating the lumped uncertainties in the system, complementary sliding mode control was combined with RWENN. RWENN was used to replace the switching control in complementary sliding mode control. RWENN can train the network parameters and adjust parameters on-line. In addition to further improve the robustness, a robust compensator was designed to compensate the parameter estimation errors of RWENN. The experimental results show that this method not only reduces the chatter of the system and guarantees the position tracking precision, but also improves the robustness of the system. © 2019, Harbin University of Science and Technology Publication. All right reserved.
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页码：102 / 109
页数：7
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