| Summary: | The environmental factors of greenhouses affect crop growth greatly and are mutually coupled and spatially distributed. Due to the complexity of greenhouse climate modeling, the current optimal control of greenhouse crop growth rarely considers the spatial distribution issues of environmental parameters. Proper Orthogonal Decomposition (POD) is a technique to reduce the order of a model by projecting it onto an orthogonal basis. In this paper, POD is used to extract environmental features from Computational Fluid Dynamics (CFD) simulations, and a low-dimensional feature subspace is obtained by energy truncation. With multi-dimensional interpolation, fast and low-dimensional reconstruction of the dynamic variation of greenhouse climates is achieved. On this basis, a rolling-horizon optimal control scheme is proposed. For each finite horizon, the external meteorological data are updated, and the response of the greenhouse environment is quickly calculated by the POD model. With the performance criterion <i>J</i> of maximizing crop production and energy efficiency, through the particle swarm optimization algorithm, the optimal settings for the greenhouse shading rate and the fan speed are derived. Such control computations are rolled forward during the whole planting season. Results of a case study show that the proposed method has low computation cost and high spacial resolution and can effectively improve the spatiotemporal accuracy of greenhouse climate management. In addition, different from traditional global optimal control methods, the proposed rolling-horizon scheme can correct various external disturbances in the procedure of crop growth, and thus it is more robust and has potential for engineering applications.
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