Estimación de parámetros y modelo de caja negra de un motor CD sin escobillas

TecnoLogicas Pub Date : 2014-07-01 DOI:10.22430/22565337.546
José Armando Becerra-Vargas, Francisco Ernesto Moreno-García, Juan J. Quiroz-Omaña, Deyanira Bautista-Arias
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引用次数: 3

Abstract

The modeling of a process or a plant is vital for the design of its control system, since it allows predicting its dynamic and behavior under different circumstances, inputs, disturbances and noise. The main objective of this work is to identify which model is best for a permanent magnet brushless DC specific motor. For this, the mathematical model of a DC motor brushless PW16D, manufactured by Golden Motor, is presented and compared with its black box model; both are derived from experimental data. These data, the average applied voltage and the angular velocity, are acquired by a data acquisition card and imported to the computer. The constants of the mathematical model are estimated by a curve fitting algorithm based on non-linear least squares and pattern search using computational tool. To estimate the mathematical model constants by non-linear least square and search pattern, a goodness of fit of 84.88% and 80.48% respectively was obtained. The goodness of fit obtained by the black box model was 87.72%. The mathematical model presented slightly lower goodness of fit, but allowed to analyze the behavior of variables of interest such as the power consumption and the torque applied to the motor. Because of this, it is concluded that the mathematical model obtained by experimental data of the brushless motor PW16D, is better than its black box model.
无刷CD电机参数估计及黑匣子模型
过程或工厂的建模对于其控制系统的设计至关重要,因为它可以预测其在不同环境、输入、干扰和噪声下的动态和行为。这项工作的主要目的是确定哪种模型最适合永磁无刷直流特定电机。为此,给出了Golden motor公司生产的直流无刷PW16D的数学模型,并与黑箱模型进行了比较;两者都是由实验数据得出的。这些数据,即平均施加电压和角速度,由数据采集卡采集并输入到计算机中。采用基于非线性最小二乘和模式搜索的曲线拟合算法估计数学模型的常数。用非线性最小二乘法和搜索法估计数学模型常数,拟合优度分别为84.88%和80.48%。黑箱模型拟合优度为87.72%。数学模型的拟合优度略低,但允许分析感兴趣的变量的行为,如功率消耗和施加在电机上的扭矩。由此得出结论,通过实验数据得到的无刷电机PW16D的数学模型优于其黑箱模型。
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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30
审稿时长
28 weeks
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