HPIPM: a high-performance quadratic programming framework for model predictive control
By Gianluca Frison, Moritz Diehl. IFAC-PapersOnLine, 2020
314 citations recorded by the source corpus.
This 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.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/5f97ad65a421fc2cfec308766f3c631442adae57
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Davide Scaramuzza
- Rien Quirynen
- Vladlen Koltun
- Matthias Müller
- Gianluca Frison
- Moritz Diehl
- Sergey Levine
- Hans Joachim Ferreau
In Electrical and Electronic Engineering
Citations in the corpus
- Reaching the limit in autonomous racing: Optimal control versus reinforcement learning (2023, methodology): “This allows for efficient initialization of the Newton-type solver by reusing solutions from the previous iteration, making real-time control feasible [36].”
- acados—a modular open-source framework for fast embedded optimal control (2019, methodology): “0 FalcOpt 2017 [88] NMPC MIT HPIPM 2017 [34] LQMPC, QP 2-clause BSD ODYS 2017 [18] QP proprietary protoip 2017 [53] NMPC unknown SPLIT toolbox 2017 [84] LQMPC unknown Control Toolbox 2018 [39] NMPC 2-clause BSD GRAMPC 2.”