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Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging

机译:制冷剂装料智能自动控制的模糊推理程序

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摘要

Fuzzy logic controllers are readily customizable in natural language terms and can effectively deal with nonlinearities and uncertainties in control systems. This paper presents an intelligent and automated fuzzy control procedure for the refrigerant charging of refrigerators. The elements that affect the experimental charging and the optimization of the performance of refrigerators are fuzzified and used in an inference model. The objective is to represent the intelligent behavior of a human tester and ultimately make the developed model available for the use in an automated data acquisition, monitoring, and decisionmaking system. The proposed system is capable of determining the needed amount of refrigerant in the shortest possible time. The system automates the refrigerant charging and performance testing of parallel units. The system is built using data acquisition systems from National Instruments and programmed under LabVTEW. The developed fuzzy models, and their testing results, are evaluated according to their compatibility with the principles that govern the intelligent behavior of human experts when performing the refrigerant-charging process. In addition, comparisons of the fuzzy models with classical inference models are presented. The obtained results confirm that the proposed fuzzy controllers outperform traditional crisp controllers and provide major test time and energy savings. The paper includes thorough discussions, analysis, and evaluation.
机译:模糊逻辑控制器很容易以自然语言术语进行自定义,并且可以有效处理控制系统中的非线性和不确定性。本文提出了一种用于冰箱制冷剂充注的智能自动化模糊控制程序。对影响实验充电和优化冰箱性能的因素进行了模糊化,并在推理模型中使用。目的是代表人类测试人员的智能行为,并最终使开发的模型可用于自动化数据采集,监视和决策系统。所提出的系统能够在最短的时间内确定所需的制冷剂量。该系统可自动执行并联装置的制冷剂充注和性能测试。该系统使用National Instruments的数据采集系统构建,并在LabVTEW下进行编程。根据所开发的模糊模型及其测试结果与执行制冷剂加注过程时支配人类专家的智能行为的原理的兼容性进行评估。另外,提出了模糊模型与经典推理模型的比较。获得的结果证实,所提出的模糊控制器优于传统的脆控制器,并提供了大量的测试时间和节能效果。本文包括详尽的讨论,分析和评估。

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