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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Manring, LH; Schultze, JF; Zimmerman, SJ; Mann, BP

Published Date

  • January 1, 2022

Published In

Volume / Issue

  • 182 /

Electronic International Standard Serial Number (EISSN)

  • 1096-1216

International Standard Serial Number (ISSN)

  • 0888-3270

Digital Object Identifier (DOI)

  • 10.1016/j.ymssp.2022.109574

Citation Source

  • Scopus