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On the Development of the En Masse Elimination Algorithm for Actuator Grouping Optimization in Adaptive Structures

[+] Author Affiliations
Jeffrey R. Hill, K. W. Wang

University of Michigan, Ann Arbor, MI

Paper No. SMASIS2010-3655, pp. 415-420; 6 pages
  • ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 1
  • Philadelphia, Pennsylvania, USA, September 28–October 1, 2010
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-4415-1 | eISBN: 978-0-7918-3886-0
  • Copyright © 2010 by ASME


As large arrays of actuators become increasingly common in smart structures, many systems do not have enough power supplies to control each actuator individually due to design, weight, or cost constraints. This issue can be addressed by grouping multiple actuators together and powering each group with a single power supply. As this is done, it is important to determine which actuators to group together. For best performance, the grouping of actuators must be optimized. Currently, genetic algorithms and other heuristic algorithms are used to determine this grouping, but a global optimum is not guaranteed. In order to accurately group the actuators and guarantee that the global optimum is found, we have developed a new method to achieve such purpose — the En Masse Elimination (EME) technique. This is an optimization algorithm used to determine the grouping of actuators when there is a constraint on the number of power supplies, specifically when there are more actuators than power supplies present. The first step in this method is for a solution that satisfies the power supply limitation to be found. The required control authority is determined for this solution. Next, some actuators are grouped while the power supply constraint is temporarily relaxed for all the other actuators. If the control authority of this solution is no better than the control authority of the acceptable solution, than large areas of the design space can be eliminated. This process is continued until the entire design space has been searched, and the global optimum is found. Unlike other methods, the global optimum is found without having to examine every possible combination. In this paper, a detailed explanation of the EME algorithm is given and the efficacy of this method is demonstrated. An example consisting of a beam with static deformation, multiple actuators, and varying constraints on the number of power supplies is used to illustrate the concept.

Copyright © 2010 by ASME



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