Model predictive control based on particle swarm optimization of greenhouse climate for saving energy consumption

This paper presents a greenhouse climate controller, which can minimize the consumption of energy while keeping the climatic temperature variables under control. A nonlinear model predicative control (MPC) algorithm based on particle swarm optimization (PSO) is proposed in this paper, since MPC is very flexible in selecting the control objectives to solve the cost minimization problem. Combining MPC with PSO not only can state the energy cost function flexibly, but also can solve the optimization problems of the nonlinear processes. The controller consists of three fundamental elements: a predictor that predicts the temperature based on the model and process information, a cost function that assigns a value to keep the greenhouse climate condition under the minimum energy cost, and an optimization technique which uses PSO to solve the constrained nonlinear optimization problem. In this work, the proposed controller can maintain the temperature under the specified range while saving the energy consumption. The result indicates that the suggested controller is effective in energy saving. The controller has been applied to the plastic solar greenhouse located in the North of China.