arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1909.04320eess.SYcs.NEcs.SY
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Buck变换器灰箱辨识的多目标进化方法

Multi-objective Evolutionary Approach to Grey-Box Identification of Buck Converter

Faizal Hafiz, Akshya Swain, Eduardo M. A. M. Mendes, Luis Aguirre

摘要:

本研究提出了一个简单的灰箱识别方法来模拟一个真正的DC-DC降压转换器工作在连续导通模式。与观察到的动态数据,这往往是在一个相对较窄的输入范围内获得的信息空洞相关联的问题,通过利用已知的降压转换器的静态行为作为先验知识得到缓解。基于术语簇的概念,提出了一种简单的方法来确定候选模型的静态响应。然后将静态行为中的误差直接嵌入到结构选择的多目标框架中。从本质上讲,该方法将灰箱识别问题转换为一个多目标框架,以平衡模型构建的偏差-方差困境,同时明确地将先验知识集成到结构选择过程中。调查的结果,考虑到实际的降压转换器的情况下,表明它是可能的,以确定简约的模型,可以捕获系统的动态和静态行为在很宽的输入范围。

英文摘要:

The present study proposes a simple grey-box identification approach to model a real DC-DC buck converter operating in continuous conduction mode. The problem associated with the information void in the observed dynamical data, which is often obtained over a relatively narrow input range, is alleviated by exploiting the known static behavior of buck converter as a priori knowledge. A simple method is developed based on the concept of term clusters to determine the static response of the candidate models. The error in the static behavior is then directly embedded into the multi-objective framework for structure selection. In essence, the proposed approach casts grey-box identification problem into a multi-objective framework to balance bias-variance dilemma of model building while explicitly integrating a priori knowledge into the structure selection process. The results of the investigation, considering the case of practical buck converter, demonstrate that it is possible to identify parsimonious models which can capture both the dynamic and static behavior of the system over a wide input range.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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