Articles
MODELING AND CONTROL FOR CLOSED ENVIRONMENT PLANT PRODUCTION SYSTEMS
Article number
593_10
Pages
85 – 92
Language
English
Abstract
A computer program was developed to study multiple crop production and control in controlled environment plant production systems.
The program simulates crop growth and development under nominal and off-nominal environments.
Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller.
The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling.
The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting.
The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations.
Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.
The program simulates crop growth and development under nominal and off-nominal environments.
Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller.
The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling.
The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting.
The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations.
Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.
Authors
D.H. Fleisher, K.C. Ting
Keywords
model-based predictive control, crop modeling, regression analysis, Advanced Life Support Systems
Online Articles (31)