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Energy-efficient Adaptive Robust MPC for data center cooling: A convex QP approach with disturbance rejection

Author

Listed:
  • Xu, Tiexiao
  • Zheng, Hong
  • Zhu, Xiangdong
  • Li, Zhen

Abstract

To address the dual challenges of model-plant mismatch and computational complexity in data center cooling control, this paper proposes an Adaptive Robust MPC (ARMPC) framework. Online Jacobian linearization converts the optimization problem into a convex Quadratic Program (QP), avoiding the computational burden of mixed-integer mode switching. An integrated Extended State Observer (ESO) is used to estimate and reject the lumped “total disturbance” in real time. Simulation-based validation using historical operational data from a large-scale data center shows that ARMPC compensates for 88.4% of model errors and reduces energy consumption by 5.58% relative to the traditional baseline. The QP formulation is 47 times faster than the MIQP benchmark and, together with the supporting data preprocessing pipeline, leaves sufficient computational margin for future supervisory closed-loop deployment.

Suggested Citation

  • Xu, Tiexiao & Zheng, Hong & Zhu, Xiangdong & Li, Zhen, 2026. "Energy-efficient Adaptive Robust MPC for data center cooling: A convex QP approach with disturbance rejection," Energy, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:energy:v:360:y:2026:i:c:s0360544226017445
    DOI: 10.1016/j.energy.2026.141637
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