基于运动预测的近周期运动动力辅助方法

K. Hatada, K. Hirata, Takuma Sato
{"title":"基于运动预测的近周期运动动力辅助方法","authors":"K. Hatada, K. Hirata, Takuma Sato","doi":"10.1109/ICIT.2015.7125134","DOIUrl":null,"url":null,"abstract":"In this paper, we consider a synthesis procedure of power assisting systems for almost-periodic motions. In our previous study, we showed that the energy efficiency can be improved when a flat torque pattern is realized by removing the input pulsation. In this study, we apply a motion prediction technique based on the Time-Varying Seasonal AR model to the design problem of power assisting systems. The effectiveness of the proposed method is demonstrated through numerical simulations.","PeriodicalId":156295,"journal":{"name":"2015 IEEE International Conference on Industrial Technology (ICIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Power assisting method for almost-periodic motions based on motion prediction\",\"authors\":\"K. Hatada, K. Hirata, Takuma Sato\",\"doi\":\"10.1109/ICIT.2015.7125134\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we consider a synthesis procedure of power assisting systems for almost-periodic motions. In our previous study, we showed that the energy efficiency can be improved when a flat torque pattern is realized by removing the input pulsation. In this study, we apply a motion prediction technique based on the Time-Varying Seasonal AR model to the design problem of power assisting systems. The effectiveness of the proposed method is demonstrated through numerical simulations.\",\"PeriodicalId\":156295,\"journal\":{\"name\":\"2015 IEEE International Conference on Industrial Technology (ICIT)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Industrial Technology (ICIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2015.7125134\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Industrial Technology (ICIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2015.7125134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

本文考虑了近周期运动的动力辅助系统的综合过程。在我们之前的研究中,我们证明了通过消除输入脉动来实现平坦转矩模式可以提高能效。在本研究中,我们将基于时变季节AR模型的运动预测技术应用于动力辅助系统的设计问题。通过数值仿真验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power assisting method for almost-periodic motions based on motion prediction
In this paper, we consider a synthesis procedure of power assisting systems for almost-periodic motions. In our previous study, we showed that the energy efficiency can be improved when a flat torque pattern is realized by removing the input pulsation. In this study, we apply a motion prediction technique based on the Time-Varying Seasonal AR model to the design problem of power assisting systems. The effectiveness of the proposed method is demonstrated through numerical simulations.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信