Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems

Marco Lepri, Davide Bacciu*, Cosimo Della Santina

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

This letter concerns control-oriented and structure-preserving learning of low-dimensional approximations of high-dimensional physical systems, with a focus on mechanical systems. We investigate the integration of neural autoencoders in model order reduction, while at the same time preserving Hamiltonian or Lagrangian structures. We focus on extensively evaluating the considered methodology by performing simulation and control experiments on large mass-spring-damper networks, with hundreds of states. The empirical findings reveal that compressed latent dynamics with less than 5 degrees of freedom can accurately reconstruct the original systems' transient and steady-state behavior with a relative total error of around 4%, while simultaneously accurately reconstructing the total energy. Leveraging this system compression technique, we introduce a model-based controller that exploits the mathematical structure of the compressed model to regulate the configuration of heavily underactuated mechanical systems.

Original languageEnglish
Pages (from-to)133-138
Number of pages6
JournalIEEE Control Systems Letters
Volume8
DOIs
Publication statusPublished - 2024

Keywords

  • autoencoders
  • Hamiltonian dynamics
  • model order reduction

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