Neuron adaptive PID control for greenhouse environment

In this article, a single neuron adaptive proportional, integral, and derivative (PID) control scheme is proposed for a greenhouse environment control problem by employing Hebb learning algorithm for tuning the parameters of the controller. The proposed scheme takes the advantage of the ability of neuron, such as adaptivity, self-organizing, and selflearning, and it is easily implemented and has been successfully applied to a greenhouse climate control problem. The results show that the proposed adaptive PID control scheme can provide better closed-loop performance compared with the conventional PID method.