TITLE:
Design of Soft Computing Based Optimal PI Controller for Greenhouse System
AUTHORS:
A. Manonmani, T. Thyagarajan, S. Sutha, V. Gayathri
KEYWORDS:
Greenhouse System, Feedback-Feed Forward Linearization and Decoupling, IMC Based PI Controller, Genetic Algorithm, Particle Swarm Optimization, Nonlinear System
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.11,
September
6,
2016
ABSTRACT: Greenhouse
system (GHS) is the worldwide fastest growing phenomenon in agricultural
sector. Greenhouse models are essential for improving control efficiencies. The
Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high
nonlinear interactions between the biological subsystem and the physical subsystem
and 2) strong coupling between the process variables such as temperature and
humidity. In this paper, a decoupled linear cooling model has been developed
using a feedback-feed forward linearization technique. Further, based on the
model developed Internal Model Control (IMC) based Proportional Integrator (PI)
controller parameters are optimized using Genetic Algorithm (GA) and Particle
Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The
closed loop control is carried out using the above control schemes for
set-point change and disturbance rejection. Finally, closed loop servo and
servo-regulatory responses of GHS are compared quantitatively as well as
qualitatively. The results implicate that IMC based PI controller using PSO
provides better performance than the IMC based PI controller using GA. Also, it
is observed that the disturbance introduced in one loop will not affect the
other loop due to feedback-feed forward linearization and decoupling. Such a
control scheme used for GHS would result in better yield in production of crops
such as tomato, lettuce and broccoli.