Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics
Seminar Series：Advanced Robotics & MEMS
报告人： 邹清泽 博士
题目：High-Speed Imaging and Broadband Nanomechanical Quantification on Scanning Probe Microscope: A Learning-Control-based Approach
Abstract：Scanning probe microscope (SPM) has become the enabling tool to image, characterize, modify, and manipulate a wide spectrum of materials at nano- to atomic scale. However, due to its serial operation nature, SPM has been limited in its imaging speed to interrogate dynamic phenomena at nanoscale and molecular level, as well as the frequency range of nanomechanicalmeasurementto quantitatively characterizetheevolutions of material properties during dynamic processes occurring in seconds or even faster. These challenges exist due to factors including the excitation of the intertwined dynamics and hysteresis effects of piezoelectric actuators, the nonlinear probe-sample interaction dynamics, the distributive hydrodynamic disturbance force, and the need of applied force to rapidly excite the material properties (not others). In this talk, I will present a suite of inversion-based iterative-learning- control (ILC) tools to achievehigh-speed SPM imaging, rapid broadband nanomechanical quantifications of soft and live biological materials, and high-speed probe-based nanofabrication. Then I will conclude the talk by discussing our recent efforts in extending the ILC beyond repetitive applications by combining offlinea priori learning via ILC with online synthesis, first, for linear systems, and then, for simultaneous hysteresis-dynamics compensation in systems such as smart actuators.
Bibliography ：Qingze Zou is an Associate 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 output tracking and control, control tools for high-speed scanningprobe microscope imaging, probe-based nanomanufacturing, micromachining, and rapid broadbandnanomechanical measurement and mapping of soft and live biological materials. 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 the representative of the IEEE Control Systems Society in the IEEE Nanotechnology Council, a past Associate Editor of ASME Journal of Dynamic Systems, Measurement and Control, and currently a Technical Editor of IEEE/ASME Transactions on Mechatronics.