纳米定位阶段具有可学习带扩展的迭代学习控制

IF 1.9 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Assembly Automation Pub Date : 2022-09-14 DOI:10.1108/aa-03-2022-0070
Chengsi Huang, Zhi-Heng Yang, Jiedong Li
{"title":"纳米定位阶段具有可学习带扩展的迭代学习控制","authors":"Chengsi Huang, Zhi-Heng Yang, Jiedong Li","doi":"10.1108/aa-03-2022-0070","DOIUrl":null,"url":null,"abstract":"Purpose Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":"1 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An iterative learning control with learnable band extension for the nanopositioning stage\",\"authors\":\"Chengsi Huang, Zhi-Heng Yang, Jiedong Li\",\"doi\":\"10.1108/aa-03-2022-0070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.\",\"PeriodicalId\":55448,\"journal\":{\"name\":\"Assembly Automation\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2022-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Assembly Automation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1108/aa-03-2022-0070\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Assembly Automation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1108/aa-03-2022-0070","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

摘要压电驱动纳米定位平台具有响应速度快、定位精度高、刚度大等优点,在微纳加工领域得到了广泛的应用。然而,由于压电作动器固有的非线性滞后,使得纳米定位平台的定位精度大大降低。此外,在应用中,纳米定位阶段总是以重复轨迹作为参考信号进行,这使得磁滞行为具有周期性。为此,提出了一种自适应谐振抑制迭代学习控制(ARS-ILC)来解决磁滞效应。从而提高了纳米定位平台的定位精度。设计/方法/方法迟滞行为由Prandtl-Ishlinskii模型确定。通过建立收敛函数,证明了ILC的可学习频带受到纳米定位阶段轻阻尼共振的限制。然后,采用约束极点和零点的自适应陷波滤波器(ANF)抑制谐振峰;最后,采用在线稳定性监督(OSS)保证估计频率收敛于谐振频率。在纳米定位阶段进行了一系列实验,结果验证了OSS可以保证ANF的收敛性。此外,通过ARS-ILC扩展了可学习频带;因此,纳米定位阶段的迟滞行为被取消。由于ARS-ILC精度高,易于实现,不仅可以用于纳米定位阶段控制,还可以用于其他重复性运动的制造过程控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An iterative learning control with learnable band extension for the nanopositioning stage
Purpose Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.
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
Assembly Automation
Assembly Automation 工程技术-工程:制造
CiteScore
4.30
自引率
14.30%
发文量
51
审稿时长
3.3 months
期刊介绍: Assembly Automation publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of assembly technology and automation, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of industry developments. All research articles undergo rigorous double-blind peer review, and the journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations.
期刊最新文献
The welding tracking technology of an underwater welding robot based on sliding mode active disturbance rejection control The application of robotics and artificial intelligence in embroidery: challenges and benefits Online modeling of environmental constraint region for complex-shaped parts assembly Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantization Automatic tolerance analyses by generation of assembly graph and mating edges from STEP AP 242 file of mechanical assembly
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1