Friday, October 16 • 2:30 PM – PWEB, Rm. 175

Learning-based High-speed Motion Control: Application to Scanning Probe Microscopy

Prof. Qingze Zou

Rutgers University

Abstract: Iterative learning control (ILC) has demonstrated its superior efficiency, efficacy, and robustness in a broad variety of applications including high-speed, broadband motion control. A fundamental limitation of ILC framework, however, is that the applied operations need to be of repetitive nature with the desired motion (trajectory) fixed and known a priori. As a result, ILC cannot be applied in non-repetitive applications such as probe-based nanomanufacturing and nanomanipulation where the desired trajectory is not completely known a priori. Existing efforts to extend ILCs beyond repetitive operations to general motion control, however, are challenged by the limited applications and limited types of trajectories that can be tracked. In this talk, I will first use scanning probe microscope (SPM) as an example to illustrate a suite of recently-developed inversion-based iterative learning control algorithms in achieving high-speed SPM imaging, rapid broadband nanomechanical quantifications of soft and live biological materials, and high-speed probe-based nanofabrication. Second, I will present our efforts in extending the ILC beyond repetitive applications, by combining offline a priori learning via ILC with online synthesis, first for linear systems, and then for simultaneous hysteresisdynamics compensation in systems such as smart actuators.

Biographical Sketch: Dr. 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 scanning probe microscope imaging, probe-based nanomanufacturing, micromachining, and rapid broadband nanomechanical 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 former Associate Editor of ASME Journal of Dynamic Systems, Measurement and Control, and currently a Technical Editor of IEEE/ASME Transactions on Mechatronics.

For additional information, please contact Prof. Xinyu Zhao at (860) 486-0241, xinyuz@engr.uconn.edu or Laurie Hockla at (860) 486-2189, hockla@engr.uconn.edu

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