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【百家大讲堂】第127期:High-speed Precision Motion Control via Basis Functions: Nanopositioning Applications

编辑: 研究生院 发布日期: 2018-11-01 浏览量:

     讲座题目High-speed Precision Motion Control via Basis Functions: Nanopositioning Applications

  报 告 人:Qingze Zou 邹清泽 教授

  时  间:2018年11月7日(周三)9:00

  地  点:中关村校区研究生教学楼5楼创新基地(电梯出口北侧)

  主办单位:研究生院、自动化学院

  报名方式:登录北京理工大学微信企业号---第二课堂---课程报名中选择“百家大讲堂第127期:High-speed Precision Motion Control via Basis Functions: Nanopositioning Applications

【主讲人简介】

 

  邹清泽,新泽西州立罗格斯大学,机械与航天系教授,曾任职于爱荷华州立大学机械工程系。邹教授于2003年在华盛顿大学机械工程系获得博士学位,其研究兴趣包括:基于学习的高精度运动控制,高速扫描探头显微技术,软体及活体样本的高速、宽频纳米机械测试、高速纳米制造、智能及软执行机构的先进控制、工业机器人控制。

  邹教授于2009年获得美国国家科学基金会颁发的职业奖,2010年获得美国自动控制委员会颁发的O Hugo Schuck最佳论文奖。邹教授曾任Journal of Dynamic Systems, Measurement and Control编辑,现任IEEE/ASME Transactions on Mechatronics, Control Engineering Practices, Mechatronics期刊的技术编辑. 邹教授是美国机械工程师协会的会士。

  

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.

 

【讲座摘要】

  高速精密运动控制在很多应用中是必不可少的,从纳米级光刻,扫描探针显微镜(SPM),到增材制造等应用场合,都对运动控制的高速和高精度提出越来越高的要求。然而,这些应用带来的挑战性问题还没有得到令人满意的解决:需要在存在不利的非线性和动态效应的情况下(例如非最小相位零点)实现高速高精度轨迹跟踪、提高时变、不确定性系统的鲁棒性。需要跟踪的期望轨迹可以是任意的、高速的、无先验知识的,跟踪轨迹与输出转换之间可能存在非周期性切换。在本次报告中,我们将介绍一种基于学习的方法来应对这些挑战,这种方法是基于迭代学习控制(ILC)框架和叠加原理的组合与扩展。我们将通过高速纳米定位、高速SPM成像和基于探针的纳米加工等实验过程进行讨论,并展示相关结果。

  
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.