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A fuzzy relational identification algorithm and its application to predict the behavior of a motor drive system

机译：模糊关系识别算法及其在电机驱动系统性能预测中的应用

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摘要

Fuzzy relational identification builds a relational model describing a system's behavior by a nonlinear mapping between its variables. In this paper, we propose a new fuzzy relational algorithm based on the simplified max-min relational equation. The algorithm presents an adaptation method applied to the gravity-center of each fuzzy set based on the error integral value between the measured and predicted system's output, and uses the concept of time-variant universe of discourse. The identification algorithm also includes a method to attenuate the noise influence in the extracted system's relational model using a fuzzy filleting mechanism. The algorithm is applied to a one-step forward prediction of a simulated and experimental motor drive system. The identified model has its input-output variables (stator-reference current and motor speed signal) treated as fuzzy sets, whereas the relations existing between them are described by means of a matrix R defining the relational model extracted by the algorithm. The results show the good potentialities of the algorithm in predicting the behavior of the system and in attenuating through the fuzzy filtering method possible noise distortions in the relational model.
机译：模糊关系识别可通过变量之间的非线性映射来建立描述系统行为的关系模型。在本文中，我们提出了一种基于简化的最大-最小关系方程的新的模糊关系算法。该算法基于被测系统和预测系统输出之间的误差积分值，提出了一种适用于每个模糊集重心的自适应方法，并使用了时变话语世界的概念。识别算法还包括使用模糊圆角化机制来衰减提取的系统的关系模型中的噪声影响的方法。该算法应用于模拟和实验电机驱动系统的一步式正向预测。所识别的模型将其输入-输出变量（定子参考电流和电动机速度信号）视为模糊集，而它们之间存在的关系则通过定义了算法提取的关系模型的矩阵R来描述。结果表明，该算法在预测系统行为以及通过模糊滤波方法衰减关系模型中可能出现的噪声失真方面具有良好的潜力。

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