文章基本信息

标题：HPIPM: a high-performance quadratic programming framework for model predictive control ⁎
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作者：Gianluca Frison ; Moritz Diehl
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：6563-6569
DOI：10.1016/j.ifacol.2020.12.073
语种：English
出版社：Elsevier
摘要：AbstractThis paper introduces HPIPM, a high-performance framework for quadratic programming (QP), designed to provide building blocks to efficiently and reliably solve model predictive control problems. HPIPM currently supports three QP types, and provides interior point method (IPM) solvers as well (partial) condensing routines. In particular, the IPM for optimal control QPs is intended to supersede the HPMPC solver, and it largely improves robustness while keeping the focus on speed. Numerical experiments show that HPIPM reliably solves challenging QPs, and that it outperforms other state-of-the-art solvers in speed.
关键词：Keywordsquadratic programmingmodel predictive controlembedded optimizationsoftware