TY - JOUR
T1 - A decoupled inversion-based iterative control approach to multi-axis precision positioning
T2 - 3D nanopositioning example
AU - Yan, Yan
AU - Wang, Haiming
AU - Zou, Qingze
N1 - Funding Information:
Yan Yan was a graduate student in Qingze Zou’s research group during this work. Yan Yan was born in Xi’an, China. She obtained the Bachelors degree in Automatic Control from Northwestern Polytechnic University, Xi’an, China in 2007, and the Master of Science in Mechanical Engineering from Iowa State University (ISU) in 2009. Currently, she is a Ph.D. candidate in the Mechanical Engineering Department of Johns Hopkins University. She received the Graduate Research Excellence Award from ISU in 2009, and the O. Hugo Schuck Best Paper Award from the American Automatic Control Council (AACC) in 2010. Haiming Wang received his B.S. degree in Precision Instruments, Measurement and Control from Hefei University of Technology, Hefei, China, in 2005, and the M.S. degree in Precision Machinery and Precision Instrumentation from the University of Science and Technology of China, Hefei, China, in 2008, respectively. He is currently working toward the Ph.D. degree in the Department of Mechanical and Aerospace Engineering, Rutgers, the State University of New Jersey. His research interests include iterative learning control, inversion-based output tracking theory, and high-speed nanomanipulation. Qingze Zou received his Ph.D. in Mechanical Engineering from the University of Washington, Seattle, WA in Fall 2003. He obtained a M.S. degree in Mechanical Engineering from Tsinghua University, Beijing, China in 1997 and a Bachelors degree in Automatic Control from the University of Electronic Science and Technology of China in 1994. Currently he is an Associate professor in the Mechanical & Aerospace Engineering Department, Rutgers, the State University of New Jersey. Previously he taught in the Mechanical Engineering Department of Iowa State University. His research interests are in inversion-based output tracking and path-following, control tools for high-speed scanning probe microscope imaging, probe-based nanomanufacturing, and micro-machining, and rapid broadband nanomechanical measurement and mapping of soft materials. He received the NSF CAREER award in 2009, and the O. Hugo Schuck Best Paper Award from the American Automatic Control Council (AACC) in 2010.
PY - 2012/1
Y1 - 2012/1
N2 - System inversion provides a nature avenue to utilize the priori knowledge of system dynamics in iterative learning control, resulting in rapid convergence and exact tracking (for nonminimum-phase systems). The benefits of system inversion, however, are not fully exploited in the time-domain ILC approach due to the lack of uncertainty quantification. This critical limit was alleviated in the frequency-domain formulated inversion-based iterative control (IIC) techniques. The existing IIC techniques, however, are for single-inputsingle- output (SISO) systems only, and the time-domain properties of the IIC techniques are unclear. The contributions of the proposed multi-axis inversion-based iterative control (MAIIC) approach are twofold: First, the IIC technique is extended from SISO systems to multi-inputmulti-output systems and is easy to implement in practice. The iterative control law is optimized by using the quantification of the system uncertainty. Secondly, the time-domain properties of the MAIIC law are discussed. The proposed MAIIC technique is illustrated through 3D nanopositioning experiments using piezoelectric actuators. The experimental results clearly demonstrated that by using the proposed technique, precision tracking in all 3D axes can be achieved in the presence of a pronounced cross-axis dynamics coupling effect.
AB - System inversion provides a nature avenue to utilize the priori knowledge of system dynamics in iterative learning control, resulting in rapid convergence and exact tracking (for nonminimum-phase systems). The benefits of system inversion, however, are not fully exploited in the time-domain ILC approach due to the lack of uncertainty quantification. This critical limit was alleviated in the frequency-domain formulated inversion-based iterative control (IIC) techniques. The existing IIC techniques, however, are for single-inputsingle- output (SISO) systems only, and the time-domain properties of the IIC techniques are unclear. The contributions of the proposed multi-axis inversion-based iterative control (MAIIC) approach are twofold: First, the IIC technique is extended from SISO systems to multi-inputmulti-output systems and is easy to implement in practice. The iterative control law is optimized by using the quantification of the system uncertainty. Secondly, the time-domain properties of the MAIIC law are discussed. The proposed MAIIC technique is illustrated through 3D nanopositioning experiments using piezoelectric actuators. The experimental results clearly demonstrated that by using the proposed technique, precision tracking in all 3D axes can be achieved in the presence of a pronounced cross-axis dynamics coupling effect.
KW - Dynamics coupling
KW - Iterative learning control
KW - Nanopositioning control
KW - System inversion
UR - http://www.scopus.com/inward/record.url?scp=84355166644&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84355166644&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2011.09.032
DO - 10.1016/j.automatica.2011.09.032
M3 - Article
AN - SCOPUS:84355166644
SN - 0005-1098
VL - 48
SP - 167
EP - 176
JO - Automatica
JF - Automatica
IS - 1
ER -