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作 者:李锋[1] 樊玉和 梁辉[1] LI Feng;FAN Yuhe;LIANG Hui(Institute of Mechanical and Electrical,Qingdao University of Science and Technology,Qingdao 266061)
出 处:《计算机与数字工程》2021年第5期908-913,986,共7页Computer & Digital Engineering
基 金:国家自然科学基金项目(编号:51605238);山东省重点研发计划项目(编号:2017GGX30108);青岛市源头创新计划应用基础研究项目(编号:17-1-1-92-jch);山东省高校科技计划项目(编号:J16LB07)资助。
摘 要:温室环境是一种复杂多变的多因素控制对象,传统BP神经网络PID控制器对温室因素控制时的权值不够精确,对温室内温湿度控制欠缺。现设计了一种采用遗传—粒子群算法来优化传统的BP神经网络算法的PID控制器,用来更精准地测控温室内的温度湿度。该系统采用COMFAST嵌入式智能网关作为整个控制系统的控制核心,经过BME280温湿度传感器组成的气象台搜索温室内外温度湿度等数据信息,通过RS485串口通讯传递给网关,网关根据设定的范围控制执行装备,从而对温室的温度湿度进行精准控制,提高温室蔬菜的产值增值。试验结果表明:改进后的BP神经网络PID控制器比传统的BP神经网络PID控制器对温室内蔬菜温度湿度控制效果更接近蔬菜最佳生长的温湿度。The greenhouse environment is a complex and variable multi-factor control object.The traditional BP neural net⁃work PID controller is not accurate enough to control the greenhouse factors,and lacks control of temperature and humidity in the greenhouse.A PID controller using genetic-particle swarm optimization to optimize the traditional BP neural network algorithm is de⁃signed to more accurately measure the temperature and humidity in the greenhouse.The system adopts COMFAST embedded intelli⁃gent gateway as the control core of the whole control system.The weather station composed of BME280 temperature and humidity sensor searches for temperature information such as temperature and humidity inside and outside the greenhouse,and transmits it to the gateway through RS485 serial communication.The gateway controls the execution equipment according to the set range.The temperature and humidity of the greenhouse are accurately controlled to increase the value of greenhouse vegetables.The experimen⁃tal results show that the improved BP neural network PID controller is more effective than the traditional BP neural network PID con⁃troller in controlling the temperature and humidity of vegetables in the greenhouse.
关 键 词:COMFAST嵌入式智能网关 遗传算法—粒子群算法 BME280温湿度控制器 精确
分 类 号:TN06[电子电信—物理电子学]
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