用智能模糊控制器控制假手

Asma. R. Qishqish, A. M. EIbreki, T. H. Elmenfy, Zakariya Rajab
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引用次数: 2

摘要

在过去的几十年里,控制假手技术吸引了众多的专业人士和研究人员。利用主动和被动技术来提高义肢手部控制的效率是许多研究的重点。但是,文献中仍然缺少一环信息。因此,本文旨在对比例积分导数控制器(PID),模糊逻辑控制器FLC和遗传算法模糊逻辑控制器(GAFLC)的参数信息进行全面的比较。利用人工智能(AI)和智能控制技术。PID控制器的输出响应在设计时能够满足要求,但是;与FLC系统相比,PID系统的性能时间有所增加,但没有明显增加。当PID控制器应用于工业手控系统时,会产生突发性反应。理论上,高海拔时间可能对患者手指极为有害MP=42.1%。大超限是任何工程应用中另一个不希望出现的控制现象,特别是当应用程序需要将其应用于人体物体时。因此,进一步研究这些问题对于使用FLC和GAFLC至关重要,MP=6.4%, MP=4.7%。这项研究的成果在总结的经验教训一节中作了重点介绍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prosthetic Hand Controlled Via Intelligent Fuzzy Controller
During the last decades, Control Prosthetic Hand technology attracted numerous professionals and researchers. There are many R&D focused on increasing the efficiency of Prosthesis hand control by using active and passive techniques. But, the literature is still a missing ring of the information. Hence, this paper aims to respond comparison between the Proportional Integral Derivative controller (PID), Fuzzy logic controller FLC, and Genetic Algorithm Fuzzy Logic Controller (GAFLC) thoroughly the information regarding the parameters. Using artificial intelligence (AI) and intelligent control techniques. The output response with the PID controller was able to meet the requirements during the design, but; The PID system performance time was increased without a significant increase compared with the FLC system. A sudden reaction was produced when the PID controller is used in the industrial hand system. In theory, a high altitude time may be extremely harmful to the patient's finger MP=42.1%. Large overrun is another undesirable control phenomenon in any engineering application, especially when the application is required to apply it to human objects. Therefore, further R&D on these issues has been essential at using FLC and GAFLC, MP=6.4%, MP=4.7%. The outcomes of the study are highlighted in the lessons learned section.
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