Neuro-dynamic programming-based optimal control for crop growth in precision agriculture

• Published in: Proceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004 pp. 397 - 402
• Main Authors: Patino, H., Pucheta, J., Fullana, R., Schugurensky, C., Kuchen, B.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway, NJ IEEE 2004; IEEE Service Center
• Subjects: Actuators; Agriculture; Applied sciences; Computer science; control theory; systems; Control system synthesis; Control systems; Control theory. Systems; Costs; Crops; Exact sciences and technology; Mechanical engineering. Machine design; Neurocontrollers; Optimal control; Performance analysis; Production management; Research and development; Production system; Optimal growth; Suboptimal control; Optimal policy; Control program; Minimization; Neural network; Scale model; Research and development; Neurocontrollers; Production management; Scaling law; Economy; Optimal control; Agriculture; Feasibility; Greenhouse; Actuator; Intelligent control; State constraint
• ISBN: 0780386353; 9780780386358
• ISSN: 2158-9860; 2158-9879
• DOI: 10.1109/ISIC.2004.1387716

The agricultural sector is one activity of the major importance in the Argentinean economy, and their production management and control systems are an important subject of research and development. A neuro-dynamic programming based optimal controller for crop-greenhouse systems is proposed. The neurocontroller drives the crop-growth development minimizing a predefined performance index, which considers minimization of the greenhouse operative costs and the final state errors under physical constraints on process variables and actuator signals. In particular, it is applied to guide the tomato seedling crop development through control of a greenhouse microclimate. In the neurocontroller design process nonlinear dynamic behavior of the crop greenhouse system and the July climate data of 1999 of San Juan, Argentina, are considered. The obtained control law is suboptimal due to the use of neural networks to approximate both the optimal cost-to-go function and optimal policy. In order to show the practical feasibility and performance of the proposed neurocontroller, simulation studies were carried out for the tomato-seedling crop development, which would ease the transition to experimentations on a scale model of a greenhouse available in the Instituto de Automatical Laboratory.