Kavli Affiliate: Biao Huang
| First 5 Authors: Zhiyinan Huang, Jinfeng Liu, Biao Huang, ,
| Summary:
Water scarcity is an urgent issue to be resolved and improving irrigation
water-use efficiency through closed-loop control is essential. The complex
agro-hydrological system dynamics, however, often pose challenges in
closed-loop control applications. In this work, we propose a two-layer neural
network (NN) framework to approximate the dynamics of the agro-hydrological
system. To minimize the prediction error, a linear bias correction is added to
the proposed model. The model is employed by a model predictive controller with
zone tracking (ZMPC), which aims to keep the root zone soil moisture in the
target zone while minimizing the total amount of irrigation. The performance of
the proposed approximation model framework is shown to be better compared to a
benchmark long-short-term-memory (LSTM) model for both open-loop and
closed-loop applications. Significant computational cost reduction of the ZMPC
is achieved with the proposed framework. To handle the tracking offset caused
by the plant-model-mismatch of the proposed NN framework, a shrinking target
zone is proposed for the ZMPC. Different hyper-parameters of the shrinking zone
in the presence of noise and weather disturbances are investigated, of which
the control performance is compared to a ZMPC with a time-invariant target
zone.
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