講座題目：High-speed Precision Motion Control via Basis Functions: Nanopositioning Applications

　　報 告 人：Qingze Zou 鄒清澤 教授

　　時　 間：2018年11月7日(周三)9:00

　　地　 點：中關村校區(qū)研究生教學樓5樓創(chuàng)新基地（電梯出口北側）

　　主辦單位：研究生院,、自動化學院

　　報名方式：登錄北京理工大學微信企業(yè)號---第二課堂---課程報名中選擇“百家大講堂第127期：High-speed Precision Motion Control via Basis Functions: Nanopositioning Applications”

【主講人簡介】

　　鄒清澤，新澤西州立羅格斯大學，機械與航天系教授,，曾任職于愛荷華州立大學機械工程系,。鄒教授于2003年在華盛頓大學機械工程系獲得博士學位，其研究興趣包括：基于學習的高精度運動控制,，高速掃描探頭顯微技術,，軟體及活體樣本的高速、寬頻納米機械測試,、高速納米制造,、智能及軟執(zhí)行機構的先進控制、工業(yè)機器人控制,。

　　鄒教授于2009年獲得美國國家科學基金會頒發(fā)的職業(yè)獎,，2010年獲得美國自動控制委員會頒發(fā)的O Hugo Schuck最佳論文獎。鄒教授曾任Journal of Dynamic Systems, Measurement and Control編輯,，現(xiàn)任IEEE/ASME Transactions on Mechatronics, Control Engineering Practices, Mechatronics期刊的技術編輯. 鄒教授是美國機械工程師協(xié)會的會士,。

Qingze Zou is a Professor in the Department of Mechanical and Aerospace Engineering of Rutgers, the State University of New Jersey. Priorly he had taught in the Mechanical Engineering Department of Iowa State University. He obtained his Ph.D. in mechanical engineering from the University of Washington, Seattle, WA in 2003. His research interests include learning-based precision motion control, high-speed scanning probemicroscopy, rapid broadband nanomechanical mapping of soft and live samples, high-speed nanofabrication, advanced control of smart and soft actuators, and industrial robotic manipulattion. He received the NSF CAREER award in 2009, and the O Hugo Schuck Best Paper Award from the American Automatic Control Council in 2010. He is a past Associate Editor of ASME Journal of Dynamic Systems, Measurement and Control, and currently a Technical Editor of IEEE/ASME Transactions on Mechatronics, Control Engineering Practices, and Mechatronics. He is a Fellow of ASME.
 

【講座摘要】

　　高速精密運動控制在很多應用中是必不可少的，從納米級光刻,，掃描探針顯微鏡（SPM）,，到增材制造等應用場合，都對運動控制的高速和高精度提出越來越高的要求,。然而,，這些應用帶來的挑戰(zhàn)性問題還沒有得到令人滿意的解決：需要在存在不利的非線性和動態(tài)效應的情況下（例如非最小相位零點）實現(xiàn)高速高精度軌跡跟蹤、提高時變,、不確定性系統(tǒng)的魯棒性,。需要跟蹤的期望軌跡可以是任意的、高速的,、無先驗知識的,，跟蹤軌跡與輸出轉換之間可能存在非周期性切換。在本次報告中,，我們將介紹一種基于學習的方法來應對這些挑戰(zhàn),，這種方法是基于迭代學習控制（ILC）框架和疊加原理的組合與擴展。我們將通過高速納米定位,、高速SPM成像和基于探針的納米加工等實驗過程進行討論,，并展示相關結果。

　　
Abstract High-speed precision motion control is essential in a wide variety of applications, ranging from nanoscale photolithography, through scanning probe microscopy (SPM), to additive manufacturing. Continuously increasing demands for both high speed and precision in these applications, however, bring challenges that haven’t been satisfactorily resolved yet: Both high-speed precision tracking and good robustness against system variation and uncertainties need to be achieved—in the presence of adverse nonlinear and dynamics effects such as nonminumum-phase zeros; The desired trajectory to be tracked is arbitrary, at high-speed, and unknown a priori; And non-periodic switching between trajectory tracking and output transition might be involved in the operations. In this talk, we will present a learning-based approach to tackle these challenges, based on the combination and extension of the framework of iterative learning control (ILC) and the superposition principle. Experimental results in high-speed nanopositioning, and high-speed SPM imaging and probe-based nanofabrication will be discussed as illustrative examples.