基于集成卡尔曼滤波和神经网络的无刷直流电机速度控制

M. Rif'an, F. Yusivar, B. Kusumoputro
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引用次数: 8

摘要

无传感器技术在无刷直流电机上的应用主要是为了提高运行可靠性,为未来无刷直流电机的广泛应用发挥作用。本研究旨在预测负载变化,提高无传感器无刷直流电机估计结果的准确性。提出了一种基于集成卡尔曼滤波(EnKF)和神经网络的无刷直流电动机滤波算法。采用EnKF算法对转速和转子位置进行估计,并利用神经网络对扰动进行仿真估计。该算法只需要端电压和三相电流即可估计速度和扰动。建立了非线性系统的仿真模型。为了测试所提出算法的性能,给出了外部机械载荷等扰动的变化。实验结果表明,在额定转矩50%的扰动下,该算法具有良好的控制效果,误差速度为3%。仿真结果表明,该方法可以在干扰或存在干扰的情况下对速度进行跟踪和调整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensorless-BLDC motor speed control with ensemble Kalman filter and neural network
The use of sensorless technology at BLDC is mainly to improve operational reliability and play a role for wider use of BLDC motors in the future. This research aims to predict load changes and to improve the accuracy of estimation results of sensorless-BLDC. In this paper, a new filtering algorithm is proposed for sensorless brushless DC motor based on Ensemble Kalman filter (EnKF) and neural network. The proposed EnKF algorithm is used to estimate speed and rotor position, while neural network is used to estimate the disturbance by simulation. The proposed algorithm requires only the terminal voltage and the current of three phases for estimated speed and disturbance. A model of non-linear systems is carried out for simulation. Variations in disturbances such as external mechanical loads are given for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm has sufficient control with error speed of 3 % in a disturbance of 50 % of the rated-torque. Simulation results show that the speed can be tracked and adjusted accordingly either by disturbances or the presence of disturbances.
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CiteScore
0.70
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