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Special Topics in Structural Dynamics & Experimental Techniques, Volume 5 pp 55–58Cite as

Vibration Control in Meta-Structures Using Reinforcement Learning

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Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

This chapter considers using reinforcement learning (RL) to adaptively tune frequency response functions of meta-structures. RL algorithm tunes the stiffness of the spring of the lumped multi-DOF system, as the lumped mass is varied. As some of the lumped masses are modified by 10%, the spring’s stiffness is tuned to maintain the original bandgap. A Q-Learning algorithm is used for RL, wherein the Q-value is updated based on Bellman’s equation. The results compare the frequency response functions of the terminal masses of the baseline and varied mass structure.

Keywords

  • Reinforcement learning (RL)
  • Bandgap
  • Q-Learning
  • Reward function
  • Stiffness

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References

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Author information

Authors and Affiliations

  1. Vibrations, Intelligent Testing and Active Learning of Structures, Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI, USA

    D. Mehta & Vijaya V. N. Sriram Malladi

Authors
  1. D. Mehta
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  2. Vijaya V. N. Sriram Malladi
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Corresponding author

Correspondence to D. Mehta .

Editor information

Editors and Affiliations

  1. Sandia National Laboratories, Albuquerque, NM, USA

    Dr. David S. Epp

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Mehta, D., Malladi, V.N.S. (2022). Vibration Control in Meta-Structures Using Reinforcement Learning. In: Epp, D.S. (eds) Special Topics in Structural Dynamics & Experimental Techniques, Volume 5. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-75914-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-75914-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75913-1

  • Online ISBN: 978-3-030-75914-8

  • eBook Packages: EngineeringEngineering (R0)

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