High-performance small-scale solvers for linear Model Predictive Control

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2014

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In Model Predictive Control (MPC), an optimization problem needs to be solved at each sampling time, and this has traditionally limited use of MPC to systems with slow dynamic. In recent years, there has been an increasing interest in the area of fast small-scale solvers for linear MPC, with the two main research areas of explicit MPC and tailored on-line MPC. State-of-the-art solvers in this second class can outperform optimized linear-algebra libraries (BLAS) only for very small problems, and do not explicitly exploit the hardware capabilities, relying on compilers for that. This approach can attain only a small fraction of the peak performance on modern processors. In our paper, we combine high-performance computing techniques with tailored solvers for MPC, and use the specific instruction sets of the target architectures. The resulting software (called HPMPC) can solve linear MPC problems 2 to 8 times faster than the current state-of-the-art solver for this class of problems, and the high-performance is maintained for MPC problems with up to a few hundred states.
Original languageEnglish
Title of host publicationProceedings of European Control Conference (ECC) 2014
PublisherIEEE
Publication date2014
Pages128-133
DOIs
StatePublished - 2014
Event13th European Control Conference (ECC) 2014 - Strasbourg, France

Conference

Conference13th European Control Conference (ECC) 2014
Number13
LocationStrasbourg Convention and Exhibition Center
CountryFrance
CityStrasbourg
Period24/06/201427/06/2014
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

    Keywords

  • control engineering computing, linear algebra, optimisation, parallel processing, predictive control, Power, Energy and Industry Applications, Robotics and Control Systems, Signal Processing and Analysis, Transportation, high-performance computing technique, high-performance small-scale solvers, IP networks, Kernel, Libraries, linear model predictive control, linear MPC, Matrices, optimization problem, optimized linear-algebra libraries, Program processors, Registers, state-of-the-art solvers, Vectors
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