Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates
Abstract
We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network/Random Forest, which for a given parameter, predicts a strategy consisting of (1) a set of Linear Programs (LPs) such that their feasible sets form a partition of the feasible set of the MILP and (2) a candidate integer solution. For control computation and sub-optimality quantification, we solve a set of LPs online in parallel. We demonstrate our approach for a motion planning example and compare against various commercial and open-source mixed-integer programming solvers.
Cite
@article{arxiv.2303.12152,
title = {Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates},
author = {Luigi Russo and Siddharth H. Nair and Luigi Glielmo and Francesco Borrelli},
journal= {arXiv preprint arXiv:2303.12152},
year = {2023}
}
Comments
First two authors contributed equally