文章基本信息

标题：Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Hybrid Model Predictive Control ⁎
本地全文：下载
作者：Jia-Jie Zhu ; Georg Martius
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：5239-5245
DOI：10.1016/j.ifacol.2020.12.1200
语种：English
出版社：Elsevier
摘要：AbstractToday’s fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated that exact optimal control law can be computed, e.g., by mixed-integer programming (MIP) under piecewise-affine (PWA) system models. Despite the elegant theory, online solving hybrid MPC is still out of reach for many applications. We aim to speed up MIP by combining geometric insights from hybrid MPC, a simple-yet-effective learning algorithm, and MIP warm start techniques. Following a line of work in approximate explicit MPC, the proposed learning-control algorithm, LNMS, gains computational advantage over MIP at little cost and is straightforward for practitioners to implement.
关键词：KeywordsModel Predictive Control of Hybrid SystemMachine LearningLearning for ControlNonparametric learningMixed-Integer Programming