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Unified Scaling of Dynamic Optimization Design Formulations

[+] Author Affiliations
Daniel R. Herber, James T. Allison

University of Illinois at Urbana-Champaign, Urbana, IL

Paper No. DETC2017-67676, pp. V02AT03A003; 11 pages
doi:10.1115/DETC2017-67676
From:
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 43rd Design Automation Conference
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5812-7
  • Copyright © 2017 by ASME

abstract

In this article, we explore scaling in dynamic optimization with a particular focus on how to leverage scaling in design studies. Here scaling refers to the process of suitable change of variables and algebraic manipulations to arrive at equivalent forms. The necessary theory for scaling dynamic optimization formulations is presented and a number of motivating examples are shown. The presented method is particularly useful for combined physical-system and control-system design problems to better understand the relationships between the optimal plant and controller designs. In one of the examples, scaling is used to understand observed results from more complete, higher-fidelity design study. The simpler scaled optimization problem and dimensionless variables provide a number of insights. Scaling can be used to help facilitate finding accurate, generalizable, and intuitive information. The unique structure of dynamic optimization suggests that scaling can be utilized in novel ways to provide better analysis and formulations more favorable for efficiently generating solutions. The mechanics of scaling are fairly straightforward but proper utilization of scaling is heavily reliant on the creativity and intuition of the designer. The combination of existing theory and novel examples provides a fresh perspective on this classical topic in the context of dynamic optimization design formulations.

Copyright © 2017 by ASME
Topics: Design , Optimization

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