Mapping back and forth between model predictive control and neural networks
<|reference_start|>Mapping back and forth between model predictive control and neural networks: 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.<|reference_end|>
@article{drummond2024mapping,
    title={Mapping back and forth between model predictive control and neural
  networks},
    author={Ross Drummond, Pablo R Baldivieso-Monasterios, Giorgio Valmorbida},
    journal={arXiv preprint arXiv:2404.12030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12030},
    primaryClass={eess.SY cs.AI cs.SY}
}