Neural networks based on PID control for greenhouse temperature

A mathematical model of greenhouse temperature was established. Confronted with problem of greenhouse temperature control existed in conventional PID controller such as big inertia, big lag, bad adaptive ability and robustness, and other defects, a kind of intelligent PID controller based on RBF neural network with adaptive ability and self-leaning and self-organization was proposed to adjusted the parameters of PID controller. It identified the Jacohian matrix of feedback system by the RBF neural network and acquired on-line tuning the parameters of PID controller. The experimental results demonstrated that a high performance was achieve with little overshoot, steady-state precision and disturbance rejection ability.