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Arbitrary-Order Iterative Learning Control Considering H Synthesis

[+] Author Affiliations
Minghui Zheng, Liting Sun, Masayoshi Tomizuka

University of California, Berkeley, CA

Cong Wang

New Jersey Institute of Technology, Newark, NJ

Paper No. DSCC2016-9902, pp. V002T22A010; 7 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5070-1
  • Copyright © 2016 by ASME


Iterative learning control (ILC) is an effective technique to improve the tracking performance of systems through adjusting the feedforward control signal based on the memory data. It is critically important to design the learning filters in the ILC algorithm that assures the robust stability of the convergence of tracking errors from one iteration to next. The design procedure usually involves lots of tuning work especially in high-order ILC. To facilitate this procedure, this paper proposes an approach to design learning filters for an arbitrary-order ILC with guaranteed convergence and ease of tuning. The filter design problem is formulated into an H optimal control problem. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. Important characteristics of the proposed approach are explored and demonstrated on a simulated wafer scanning system.

Copyright © 2016 by ASME



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