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DOE OSTI · 2448313

Improving convergence of the Matrix Power Control Algorithm for random vibration testing

Manring, Levi H.·Schultze, John F.·Zimmerman, Sandra J.·Mann, Brian P.
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Abstract

Herein, this paper describes modifications to the Matrix Power Control Algorithm (MPCA) to improve convergence for Random Vibration Control (RVC) testing. In particular, this paper presents Multiple-Input Multiple-Output (MIMO) implementations of MPCA in simulation and experiment. An Euler–Bernoulli beam model was simulated with applied base excitations and the Box Assembly with Removable Component (BARC) was used in experiment to validate results. The Bayesian optimization package Dragonfly was used to optimize control parameters. Additionally, a moving-average was employed and optimized to improve the measured response feedback for MPCA, reduce the number of averages needed to be taken between control updates, and further improve convergence. The key results of this paper show that the performance of MPCA can be improved by tuning the control parameters and by applying an optimized moving-average. Furthermore, it is demonstrated that convergence can be achieved within 12 drive-frames, which greatly enhances vibration control capability.

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42 ENGINEERINGBayesian optimizationDragonflyconvergenceenvironmental testingmatrix powerrandom vibration

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Manring, Levi H., Schultze, John F., Zimmerman, Sandra J., Mann, Brian P.. 2022-07-27. Improving convergence of the Matrix Power Control Algorithm for random vibration testing. https://doi.org/10.1016/j.ymssp.2022.109574

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