整数规划
模型预测控制
整数(计算机科学)
分拆(数论)
计算机科学
参数统计
数学优化
集合(抽象数据类型)
计算
人工神经网络
线性规划
算法
人工智能
控制(管理)
数学
组合数学
统计
程序设计语言
作者
Luigi Russo,Siddharth H. Nair,Luigi Glielmo,Francesco Borrelli
出处
期刊:IEEE Control Systems Letters
日期:2023-01-01
卷期号:7: 2215-2220
被引量:1
标识
DOI:10.1109/lcsys.2023.3285778
摘要
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 or 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) an 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.
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