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Spatial ILC for Multi-Objective Systems

[+] Author Affiliations
Ingyu Lim, Kira L. Barton

University of Michigan, Ann Arbor, MI

David J. Hoelzle

University of Notre Dame, Notre Dame, IN

Paper No. DSCC2014-6208, pp. V002T30A003; 9 pages
doi:10.1115/DSCC2014-6208
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4619-3
  • Copyright © 2014 by ASME

abstract

Iterative learning control is an adaptive, feedforward control technique traditionally used to improve the performance of systems that execute a task repetitively. While generally applied to systems driven by temporal dynamics, there exist applications, such as additive manufacturing, for which spatial dynamics play a particularly important role in determining system behavior. To ensure high fidelity functionality for these application spaces, this paper presents a spatial learning framework for optimizing multiple performance metrics simultaneously. Utilizing a one-step optimization approach enables direct evaluation of design trade-offs over a broad range of potential solutions. The multi-objective spatial learning framework, along with stability and convergence analysis is presented. Simulation results validate the control framework.

Copyright © 2014 by ASME

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