基于径向基函数神经网络的磁悬浮系统滑模控制

2008 IEEE Conference on Robotics, Automation and Mechatronics Pub Date : 2008-11-18 DOI:10.1109/RAMECH.2008.4681421

M. Aliasghary, Abolfazl Jalilvand, M. Teshnehlab, M. A. Shoorehdeli

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引用次数: 8

摘要

本文提出了一种基于径向基函数模型的滑模控制器，用于磁悬浮系统的控制。自适应神经网络控制器需要对象的雅可比矩阵，这里采用滑动曲面和广义学习规则来解决该问题，以消除雅可比矩阵问题。仿真结果表明，该方法对磁悬浮系统的控制是可行的、有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Sliding Mode Control of Magnetic Levitation System Using Radial Basis Function Neural Networks

This paper has developed a sliding mode controller (SMC) based on a radial basis function model for control of magnetic levitation system. Adaptive neural networks controllers need plant's Jacobain, but here this problem solved by sliding surface and generalized learning rule in case to eliminate Jacobain problem. The simulation results show that this method is feasible and more effective for magnetic levitation system control.

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来源期刊

2008 IEEE Conference on Robotics, Automation and Mechatronics

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