基于高斯径向基神经网络的柔性关节滑模控制

2008 International Conference on Computer and Electrical Engineering Pub Date : 2008-12-20 DOI:10.1109/ICCEE.2008.131

F. Farivar, M. A. Shoorehdeli, M. Nekoui, M. Teshnehlab

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引用次数: 6

摘要

本文介绍了一种用于柔性关节控制的混合控制方法。推导了系统的动力学方程。所设计的控制器由线性二次调节(LQR)的经典控制器和基于高斯径向基函数神经网络(RBFNN)的滑模控制的混合控制器两部分组成。RBFNN是在控制过程中训练的，不需要离线训练。

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 Flexible Joint Using Gaussian Radial Basis Function Neural Networks

This paper, describes a hybrid control method to control a flexible joint. Dynamic equation of the system has been derived. The designed controllers consist of two parts: classical controller, which is a Linear Quadratic Regulation (LQR), and a hybrid controller,utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN). The RBFNN is trained during the control process and it is not necessary to be trained off-line.

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2008 International Conference on Computer and Electrical Engineering

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