Real-Time Multi-objective Optimal Control Algorithm for Greenhouse Environment Using Particle Swarm Optimization

The problem of greenhouse climate control has traditionally been solved by using experience based method or model based method, Moreover, light and CO₂ are seldom used in greenhouse environment control to make sure that environment state is the essential of high crop production. This paper presents a multi-objective economic optimization problem in greenhouse climate control solved in real time using a method, real-time multi-objective optimal control algorithm (MOOCA), proposed in this paper. Photosynthesis, which rate is determined by current environment (light intensity, CO₂ concentration and temperature), plays a key role on crop growth. Light compensation and CO₂ compensation are used to improve the photosynthesis rate so as to increase the crop yield. Optimization algorithm using a modified Multi-objective Particle Swarm Optimization weighs the crop yield and energy cost and gives control decision. Simulation results show the profit of real-time optimization and freedom of choice farmers make to meet their benefit demands.