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Water scarcity is an urgent issue to be resolved and improving irrigationwater-use efficiency through closed-loop control is essential. The complexagro-hydrological system dynamics, however, often pose challenges inclosed-loop control applications. In this work, we propose a two-layer neuralnetwork (NN) framework to approximate the dynamics of the agro-hydrologicalsystem. To minimize the prediction error, a linear bias correction is added tothe proposed model. The model is employed by a model predictive controller withzone tracking (ZMPC), which aims to keep the root zone soil moisture in thetarget zone while minimizing the total amount of irrigation. The performance ofthe proposed approximation model framework is shown to be better compared to abenchmark long-short-term-memory (LSTM) model for both open-loop andclosed-loop applications. Significant computational cost reduction of the ZMPCis achieved with the proposed framework. To handle the tracking offset causedby the plant-model-mismatch of the proposed NN framework, a shrinking targetzone is proposed for the ZMPC. Different hyper-parameters of the shrinking zonein the presence of noise and weather disturbances are investigated, of whichthe control performance is compared to a ZMPC with a time-invariant targetzone.

Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework