用NARMA-L2控制器控制分励直流电动机速度

Basharat Ullah, S. Hussain, M. Yousuf, F. Khan, Sumeet Khalid, Siddique Akbar, Ali Muhammad
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引用次数: 2

摘要

利用人工神经网络(ANN)的特点,设计了一种基于智能神经网络(NN)的非线性自回归移动平均(NARMA-L2)控制器,用于分励直流电机的转速控制。与传统的PI控制方法相比,所提出的方法的目的是提高单独励磁直流电机的跟踪性能。讨论了SEDM用于NARMA-L2控制器和传统PI控制器的性能比较。利用MATLAB 8.0工具箱对SEDM的整个速度控制机制进行了建模。智能NARMA-L2控制器分两步运行:第一步,执行外部负载的变化,以检查NARMA-L2的速度控制性能。第二,控制器在不同的参考速度下运行。通过与传统PI控制器和NARMA-L2控制器的比较,仿真结果表明了NARMA-L2控制器的有效性、优越性和良好的性能。优异的结果加上驱动系统的简单性,使得基于人工神经网络的NARMA-L2控制器策略非常适合广泛的应用,如工业,造纸厂等。
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 Separately Excited DC Motor Using NARMA-L2 Controller
An Intelligent Neural Network (NN) based nonlinear Autoregressive-moving average (NARMA-L2) Controller is developed for speed control of separately excited D.C. Motor by performing the features of Artificial Neural Networks (ANN). The aim of the proposed approach is to improve tracking performance of separately excited D.C. motor as compared to the conventional (PI) control approach. Performance Comparison of SEDM for NARMA-L2 controller and the conventional PI controller is also discussed. The entire speed control mechanism for SEDM is modelled by using the MATLAB 8.0 toolbox. The intelligent NARMA-L2 controller is operated in two steps: - the first, the variations in external loads is performed to check the speed control performance of NARMA-L2. The second, the controller is operated at various reference speed. Simulation results shows the effectiveness, advantages and good performance of the NARMA-L2 which is described through the comparison of conventional PI controller and NARMA-L2 controller. Excellent results added to the simplicity of the drive system, makes the ANN based NARMA-L2 controller strategy very suitable for a wide range of applications such as industries, paper mills etc.
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