Mapping back and forth between model predictive control and neural networks

Ross Drummond, Pablo R Baldivieso-Monasterios, Giorgio Valmorbida
4/18/2024
eess.SYcs.AI

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Abstract

Model predictive control (MPC) for linear systems with quadratic costs and linear constraints is shown to admit an exact representation as an implicit neural network. A method to "unravel" the implicit neural network of MPC into an explicit one is also introduced. As well as building links between model-based and data-driven control, these results emphasize the capability of implicit neural networks for representing solutions of optimisation problems, as such problems are themselves implicitly defined functions.

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Cite this paper

@article{drummond2024mapping,
  title  = {Mapping back and forth between model predictive control and neural networks},
  author = {Ross Drummond and Pablo R Baldivieso-Monasterios and Giorgio Valmorbida},
  year   = {2024},
  eprint = {2404.12030},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.12030v1}
}

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