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

被引：2

作者：

Gao, Yongsheng ^{[1
]}

Xu, Lihong ^{[1
]}

Wei, Ruihua ^{[1
]}

机构：

[1] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China

来源：

2015 8TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2 | 2015年

基金：

中国国家自然科学基金;

关键词：

greenhouse climate control; multi-objective; light compensation; CO2; compensation; particle swarm optimization; HIERARCHICAL CONTROL; MODEL; METHODOLOGY; CLIMATE; ENERGY;

D O I：

10.1109/ISCID.2015.202

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：11 / 15

页数：5

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