Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910028111282176 |
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| author | Wu, Liang Tan, Wallace Gian Yion Braatz, Richard D. Drgoňa, Ján |
| author_facet | Wu, Liang Tan, Wallace Gian Yion Braatz, Richard D. Drgoňa, Ján |
| contents | 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 $1040$ variables and $2080$ inequalities at a kHz rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18331 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates Wu, Liang Tan, Wallace Gian Yion Braatz, Richard D. Drgoňa, Ján Systems and Control 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 $1040$ variables and $2080$ inequalities at a kHz rate. |
| title | Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2602.18331 |