Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914431601999872 |
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| author | Boldocký, Ján Faulkner, Cary Michael, Elad Gulan, Martin Tuor, Aaron Drgoňa, Ján |
| author_facet | Boldocký, Ján Faulkner, Cary Michael, Elad Gulan, Martin Tuor, Aaron Drgoňa, Ján |
| contents | We present a computationally tractable framework for real-time predictive control of multi-chiller plants that involve both discrete and continuous control decisions coupled through nonlinear dynamics, resulting in a mixed-integer optimal control problem. To address this challenge, we extend Differentiable Predictive Control (DPC) -- a self-supervised, model-based learning methodology for approximately solving parametric optimal control problems -- to accommodate mixed-integer control policies. We benchmark the proposed framework against a state-of-the-art Model Predictive Control (MPC) solver and a fast heuristic Rule-Based Controller (RBC). Simulation results demonstrate that our approach achieves significant energy savings over the RBC while maintaining orders-of-magnitude faster computation times than MPC, offering a scalable and practical alternative to conventional combinatorial mixed-integer control formulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28323 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control Boldocký, Ján Faulkner, Cary Michael, Elad Gulan, Martin Tuor, Aaron Drgoňa, Ján Systems and Control We present a computationally tractable framework for real-time predictive control of multi-chiller plants that involve both discrete and continuous control decisions coupled through nonlinear dynamics, resulting in a mixed-integer optimal control problem. To address this challenge, we extend Differentiable Predictive Control (DPC) -- a self-supervised, model-based learning methodology for approximately solving parametric optimal control problems -- to accommodate mixed-integer control policies. We benchmark the proposed framework against a state-of-the-art Model Predictive Control (MPC) solver and a fast heuristic Rule-Based Controller (RBC). Simulation results demonstrate that our approach achieves significant energy savings over the RBC while maintaining orders-of-magnitude faster computation times than MPC, offering a scalable and practical alternative to conventional combinatorial mixed-integer control formulations. |
| title | Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.28323 |