秦琳琳,马娇,黄云梦,吴刚.基于积温理论的温室温度混杂系统预测控制[J].农业机械学报,2018,49(10):347-355.
QIN Linlin,MA Jiao,HUANG Yunmeng,WU Gang.Predictive Control of Greenhouse Temperature Hybrid System Based on Crop Temperature Integration Theory[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):347-355.
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基于积温理论的温室温度混杂系统预测控制   [下载全文]
Predictive Control of Greenhouse Temperature Hybrid System Based on Crop Temperature Integration Theory   [Download Pdf][in English]
投稿时间:2018-04-04  
DOI:10.6041/j.issn.1000-1298.2018.10.039
中文关键词:  温室  温度  积温  切换系统  NP-hard问题  最优化剪枝法
基金项目:国家重点研发计划项目(2017YFB0602704-2)
作者单位
秦琳琳 中国科学技术大学 
马娇 中国科学技术大学 
黄云梦 中国科学技术大学 
吴刚 中国科学技术大学 
中文摘要:温室温度系统作为典型的混杂系统,其输入包括离散的设备控制量以及可测不可控的多个室外环境扰动量。本文针对温室温度混杂系统,建立切换系统模型,基于此模型设计多输入预测控制。首先分别在4种离散状态(保温模式、自然通风模式、强制通风模式、湿帘-风机模式)下确定模型的主相关输入,采用带遗忘因子的递推最小二乘法建立子模型。然后设计预测控制器,利用双周期积温法规划预测控制设定值。求解多输入预测控制量问题为NP-hard问题,采用最优化剪枝法优化搜索。最后在实验温室应用控制算法进行实验,实验结果表明,多输入预测控制算法可以有效调控温室内温度,并且由于积温理论动态规划预测控制设定值,可减少设备的切换次数,降低能耗。
QIN Linlin  MA Jiao  HUANG Yunmeng  WU Gang
University of Science and Technology of China,University of Science and Technology of China,University of Science and Technology of China and University of Science and Technology of China
Key Words:greenhouse  temperature  temperature integration  switched systems  NP-hard problem  optimized pruning
Abstract:Greenhouse temperature system is a typical hybrid system, and its inputs include discrete equipment control quantities and a number of outdoor environmental disturbances that can be measured and not controlled. A hybrid system was proposed for greenhouse temperature, a switching system model was established, and a multi-input predictive control was designed based on this model. A determined device state can be considered as a subsystem, and the modeling of the greenhouse system can also be transformed into the modeling of all subsystems. There were numerous greenhouse environments, so it was needed to simplify the input variables. By correlation analysis, outside temperature, outside humidity and solar radiation had obviously strong correlation with inside temperature. The ARMAX model was used to describe the model, and the augmented recursive least square method with forgetting factor was used to identify the model parameters, and the model accuracy was verified. The ARMAX model was used to design a predictive control controller to solve each device’s action sequence, which was an NP-hard problem, and it was solved by optimized pruning simplifies the calculation process. At each sampling time, the set value predictive control was determined, however, under uncontrollable external environmental factors, the output fluctuations were large. If a fixed set-point was used, the system would be switched frequently, and thus increasing equipment loss. In order to solve this problem, the dual-period accumulative temperature method was utilized to dynamically adjust the set-point of predictive control according to the long-period average value of the indoor temperature and the current value, so as to reduce the unnecessary switching of the equipment and reduce the loss.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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