A high-performance Riccati based solver for tree-structured quadratic programs

Publication: Research - peer-reviewConference article – Annual report year: 2017

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Robust multi-stage Model Predictive Control (MPC) is an increasingly popular approach to handle model uncertainties due to the simplicity of its problem formulation and other attractive properties. However, the exponential growth of the problem dimensions with respect to the robust horizon renders the online solution of such problems challenging and the development of tailored solvers crucial. In this paper, an interior point method is presented that can solve Quadratic Programs (QPs) arising in multi-stage MPC efficiently by means of a tree-structured Riccati recursion and a high-performance linear algebra library. A performance comparison with code-generated and general purpose sparse QP solvers shows that the computation times can be significantly reduced for all problem sizes that are practically relevant in embedded MPC applications. The presented implementation is freely available as part of the open-source software HPMPC.
Original languageEnglish
JournalIFAC-PapersOnLine
Volume50
Issue number1
Pages (from-to)14399-14405
ISSN2405-8963
DOIs
StatePublished - 2017
Event20th IFAC World Congress 2017 - Toulouse, France

Conference

Conference20th IFAC World Congress 2017
Number20
CountryFrance
CityToulouse
Period09/07/201714/07/2017
Internet address
CitationsWeb of Science® Times Cited: 1

    Keywords

  • Predictive control, Quadratic programming, Tree structures, Numerical methods
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