Abstract

A robust model predictive control strategy based on fuzzy and neural prediction intervals is proposed to implement a greenhouse’s water and energy management system. The implementation of this model predictive control aims to optimize the energy use when controlling the irrigation process of crops based on the resources available in the greenhouse. In the dynamics considered for the greenhouse, the amount of energy available for the system’s operation is directly affected by climate conditions, such as ambient temperature and solar irradiance. Thus, the uncertainty associated with the stochastic behavior of these external disturbances can produce problems when deciding the optimal planning of energy use. Due to that, this work proposes to characterize these external signals by using prediction intervals based on fuzzy models and neural networks. Then, according to the information provided by the prediction intervals, the controller can now consider the worst-case scenarios for the energy available in the optimization problem solved by the predictive control strategy. Simulation results compare the performance of different prediction interval methods, showing their effectiveness for approximating the future behavior of the solar irradiance and ambient temperature and characterizing their uncertainty. Then, the proposed robust controllers based on the best intervals are compared with a deterministic model predictive control to show the proposal’s improvements in battery energy management