Research on Neural Network Model for Greenhouse Temperature Predictive Control

In this article, we present the application of a neural-network-based model predictive control (NNPC) scheme to control temperature in a greenhouse. We use a neural network model as the nonlinear prediction model to predict the future behavior of the controlled process. Predictive control strategy is used to optimize future behavior of the greenhouse environment by computed the controller’s optimal inputs. The effect of the algorithm is verified by simulation and an air heat exchanger is chosen as a controlled process.