A Neuro-Dynamic Programming-Based Optimal Controller for Tomato Seedling Growth in Greenhouse Systems

摘要

This work proposes a neuro-dynamic programming-based optimal controller to guide the growth of tomato seedling crops by manipulating its environmental conditions in a greenhouse. The neurocontroller manages the growth development of the crop, while minimizing a predefined cost function that considers the operative costs and the final state errors under physical constraints on process variables and actuator signals. The aim is to guide the growth of tomato seedlings by controlling the microclimate of the greenhouse. The design process of the neurocontroller considers the nonlinear dynamic behavior of the crop-greenhouse system model and the real climate data. Simulations of the proposed approach allow for contrasting its performance against those of other strategies for tomato seedling crop development subject to various climatic conditions.