Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates
Abstract
Solving large-scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman-BoxQP framework that i) learns a linear Koopman high-dimensional model, ii) eliminates the high-dimensional observables to construct a multi-step prediction model of the states and control inputs, iii) penalizes the multi-step prediction model into the objective, which results in a structured box-constrained quadratic program (BoxQP) whose decision variables include both the system states and control inputs, iv) develops a structure-exploited and warm-starting-supported variant of the feasible Mehrotra's interior-point algorithm for BoxQP. Numerical results demonstrate that Koopman-BoxQP can solve a large-scale NMPC problem with variables and inequalities at a kHz rate.
Cite
@article{arxiv.2602.18331,
title = {Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates},
author = {Liang Wu and Wallace Gian Yion Tan and Richard D. Braatz and Ján Drgoňa},
journal= {arXiv preprint arXiv:2602.18331},
year = {2026}
}
Comments
Accepted by the 8th Annual Learning for Dynamics and Control Conference (L4DC 2026). arXiv admin note: text overlap with arXiv:2602.15596