Adaptive-model predictive control of electronic expansion valves with adjustable setpoint for evaporator superheat minimization

Tesfay, Mehari; Alsaleem, Fadi; Arunasalam, Parthiban; Rao, Arvind

doi:10.1016/j.buildenv.2018.02.015

Cited by 23 publications

(1 citation statement)

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“…Hence, adaptive model predictive control (MPC) were introduced to combine the online parameter estimation and the decision of control law in the loop [20]. For example, the experiment in [42] adapts the model of evaporator in the HVAC system, which varies the setpoint of the electronics valve. [28] runs an extended Kalman filter to achieve online parameter adaptation, the estimated model is used in an MPC controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Data-driven Building Control via Bi-level Reformulation: an Experimental Result

Lian¹,

Shi²,

Koch³

et al. 2021

Preprint

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In the era of digitalization, utilization of datadriven control approaches to minimize energy consumption of residential/commercial building is of far-reaching significance. Meanwhile, A number of recent approaches based on the application of Willems' fundamental lemma for data-driven controller design from input/output measurements are very promising for deterministic LTI systems. This paper addresses the key noise-free assumption, and extends these data-driven control schemes to adaptive building control with measured process noise and unknown measurement noise via a robust bilevel formulation, whose upper level ensures robustness and whose lower level guarantees prediction quality. Corresponding numerical improvements and an active excitation mechanism are proposed to enable a computationally efficient reliable operation. The efficacy of the proposed scheme is validated by a numerical example and a real-world experiment on a lecture hall on EPFL campus.

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Section: Introductionmentioning

confidence: 99%