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Fuzzy and Neural Pbkp_rediction Intervals for Robust Control of a Greenhouse

Abstract:

A robust model pbkp_redictive control strategy based on fuzzy and neural pbkp_rediction intervals is proposed to implement a greenhouse's water and energy management system. The implementation of this model pbkp_redictive 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 pbkp_rediction intervals based on fuzzy models and neural networks. Then, according to the information provided by the pbkp_rediction intervals, the controller can now consider the worst-case scenarios for the energy available in the optimization problem solved by the pbkp_redictive control strategy. Simulation results compare the performance of different pbkp_rediction 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 pbkp_redictive control to show the proposal's improvements in battery energy management.