Mapping back and forth between model predictive control and neural networks

• URL: http://arxiv.org/abs/2404.12030v1
• Date: Thu, 18 Apr 2024 09:29:08 GMT
• Title: Mapping back and forth between model predictive control and neural networks
• Authors: Ross Drummond, Pablo R Baldivieso-Monasterios, Giorgio Valmorbida,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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