基于模型参考神经自适应控制的无刷直流电动机速度控制

Didi Susilo Budi Utomo, Ansar Rizal, A. F. O. Gaffar
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引用次数: 13

摘要

无刷直流电动机控制系统是一个多变量、非线性、强耦合的系统,具有鲁棒性和自适应能力。新兴的无刷直流电机智能控制器引起了人们的极大兴趣。神经控制是一种基于人工神经网络的控制方法,其中可用数据是测量系统动态行为的结果。这种能力非常适合应用于自适应控制系统,其中控制器由于系统行为的变化而需要自适应。利用人工神经网络建立无刷直流电机转速的逆模型。然后将该模型作为控制器。为了获得具有良好动态响应的控制方案,引入了MRAC概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model reference neural adaptive control based BLDC motor speed control
Brushless DC (BLDC) motor control system is consisted of a multi-variable, non-linear, strong-coupling system, which is used to present robust and adaptive abilities. The interest in emerging intelligent controller for BLDC motor has been increased significantly. Neural Control is an ANN (Artificial Neural Network) based control method whereby the available data is the result of measuring the dynamic behavior of the system. This capability is well suited to be applied to adaptive control systems where the controller requires adaptation due to changes in system behavior. ANN was used to build the inverse model of BLDC motor speed. This model was then used as controller. In order to obtain control schemes that have good dynamic responses, MRAC concept was applied.
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