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

• Published in: Neural processing letters Vol. 24; no. 3; pp. 241 - 260
• Main Authors: Pucheta, J., Patiño, H., Fullana, R., Schugurensky, C., Kuchen, B.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01.12.2006; Springer Nature B.V
• Subjects: Actuators; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Controllers; Cost function; Dynamic programming; Dynamical systems; Exact sciences and technology; Greenhouses; Microclimate; Nonlinear dynamics; Process variables; Tomatoes; Process planning; Minimization; Climate models; Neural network; neuro-dynamic programming; Neurocontrollers; Design process; Optimal control; Agriculture; intelligent agriculture; Dynamic programming; greenhouse control systems; Actuator; State constraint; Mathematical programming
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-006-9022-9

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.