基于人工神经网络的SEDC电机速度控制

A. Samat, Muhammad Irfan Bin Ahmad Jaafar, A. I. Tajudin, N. A. Salim, K. Daud, Nornaim Kamarudin
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引用次数: 0

摘要

本课题利用人工神经网络(ANN)设计了一种分励直流(DC)电机的转速控制。任何传统的控制器,如比例积分(PI)可以用来控制直流电机的速度。然而,传统控制器在控制直流电机速度方面的局限性在于不能准确地获得电机的实际速度并在动态条件下保持电机速度的稳定性。因此,引入人工神经网络控制器来解决另一种传统控制器的局限性问题。本课题采用神经网络对电机转速进行控制或估计,通过MATLAB/SIMULINK软件对神经网络进行训练,得到预期结果。在这个项目中,与PI控制器相比,人工神经网络已经证明了其有效控制电机速度的能力,并且在非线性系统中具有良好的性能。仿真结果表明,采用最小转速误差近似为0转/分的人工神经网络的优点和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speed Control of SEDC Motor Using Artificial Neural Network
This project designed the speed control of a separately excited direct current (DC) motor by using an Artificial Neural Network (ANN). Any conventional controller such as proportional-integral (PI) can be used to control the speed of a DC Motor. However, the limitation of the conventional controller in controlling the speed of the dc motor is inaccuracy in the ability to obtain the actual speed and maintain the stability of the motor speed in the dynamic condition. Thus, the ANN controller had been introduced to solve the problem involving the limitation of another conventional controller. The neural network is used in this project to control or estimate the motor speed by training the neural network and getting the desired result using MATLAB/SIMULINK software. In this project, the ANN has proven its ability to control motor speed compared to the PI controller effectively and has good performance in a nonlinear system. The simulation results show the advantages and efficiency of an ANN with minimum speed error which is approximately zero rpm.
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