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

TL;DR

This paper introduces a two-layer neural network framework for modeling agro-hydrological systems to improve irrigation control, achieving better accuracy and computational efficiency than existing models, and enhancing water-use efficiency.

Contribution

A novel two-layer neural network modeling framework with bias correction for agro-hydrological systems, integrated into a zone tracking model predictive controller for improved irrigation management.

Findings

Outperforms LSTM models in prediction accuracy

Reduces computational costs of model predictive control

Effective handling of plant-model mismatch with shrinking target zones

Abstract

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.