论文标题
HPIPM：模型预测控制的高性能二次编程框架
HPIPM: a high-performance quadratic programming framework for model predictive control
论文作者
论文摘要
本文介绍了HPIPM，这是二次编程的高性能框架（QP），旨在提供有效，可靠地解决模型预测性控制问题的基础。 HPIPM当前支持三种QP类型，并提供内点方法（IPM）求解器（部分）冷凝程序。特别是，用于最佳控制QPS的IPM旨在取代HPMPC求解器，并且在很大程度上提高了鲁棒性，同时保持对速度的关注。数值实验表明，HPIPM可靠地解决了具有挑战性的QP，并且它的速度优于其他最先进的求解器。
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.
