Regulation of soil moisture using zone model predictive control

Abstract

This paper concerns the input-output model identification and zone model predictive control of an agro-hydrological system modeled by a partial differential equation. The primary control objective is to maintain the soil moisture within a desired range which is suitable for grass grow. There is also a secondary control objective which is to reduce the total irrigation amount. First, a linear parameter varying (LPV) model is identified for controller design purpose using a maximum likelihood gradient-based iterative estimation method. Then, based on the LPV model, a zone model predictive control (MPC) is designed which uses an output disturbance and state observer to reduce model-plant mismatch and an asymmetric target zone to reduce irrigation amount under weather uncertainties while maintaining the soil moisture within the target range. Simulation studies show that the LPV model is a good approximation of the original nonlinear model and effectively reduces the online computational load of the MPC, and that the proposed zone MPC can lead to significant water conservation.