摘要
针对温室环境最优控制中长时域天气预测误差导致控制性能下降的问题,依据北京市生菜和能源市场价格构建直接表征控制过程经济效益的控制目标模型,将气象站采集的北京市全年天气数据作为长时域天气预测依据,采用高斯白噪声、时序反转、时间替换3种方法产生不同的天气预测误差,并将转换后的天气数据作为仿真时所用的实际天气数据,采用温室-生菜互作机理模型作为仿真计算的系统模型,研究基于双时间尺度分解的滚动时域最优控制(Receding horizon optimal control,RHOC)的控制性能,从产量、成本、经济效益等方面与传统的模型预测控制(Model predictive control,MPC)进行对比分析。结果表明:MPC仅在完美天气预测下具有较高的控制性能,在使用高斯白噪声、时序反转和时间替换3种方法生成天气预测误差情景下,RHOC相比MPC在经济效益上分别提升了1.24%、20.59%和21.32%。本研究的量化分析结果验证了双时间尺度温室环境最优控制方法的优越性,为控制算法的在线实施提供了数据支撑。
To address the problem of control performance degradation caused by long-term weather prediction errors in optimal control of greenhouse climate,a control objective model that represents the profit obtained in the control process was established based on lettuce and energy-related market prices in Beijing.Beijing’s annual weather data,collected from the weather station,were used as the reference for long-term weather predictions.Three methods,including Gaussian white noise,time series reversal,and time substitution,were used to generate weather prediction errors.These transformed weather data were then used as real weather data for simulations.A greenhouse-lettuce mechanistic model was taken as the system model for simulations.The control performance was analyzed for the two-time-scale decomposed Receding Horizon Optimal Control(RHOC).Its yield,cost,and profit were compared with those from the traditional Model Predictive Control(MPC).The results showed that:MPC exhibited superior performance only under perfect weather predictions,whereas RHOC improved the profit by 1.24%,20.59%,and 21.32%respectively compared to MPC in scenarios with Gaussian white noise,time series reversal,and time substitution.The quantitative analysis in this study confirmed the superiority of the two-time-scale optimal control of greenhouse climate,and provided data support for the online implementation of control algorithms.
作者
徐丹
古卓朋
冉亚平
王书胜
徐雷
王明钦
马浚诚
XU Dan;GU Zhuopeng;RAN Yaping;WANG Shusheng;XU Lei;WANG Mingqin;MA Juncheng(College of Water Resources and Civil Engineering,China Agricultural University,Beijing 100083,China;Institute of Urban Agriculture,Chinese Academy of Agricultural Sciences,Chengdu 610213,China;Jiangxi Daduo Technology Co.,Ltd.,Nanchang 330029,China;Shouguang Hengshu Wujiang Agricultural Development Group Co.,Ltd.,Shouguang 262700,China)
出处
《中国农业大学学报》
北大核心
2026年第2期183-191,共9页
Journal of China Agricultural University
基金
山东省重点研发计划(2022CXGC020708)
国家重点研发计划(2024YFD2000800)
国家自然科学基金项目(32371998)
现代农业产业技术体系(CARS-23-D02)
北京市设施蔬菜创新团队项目(BAIC01-2025)
中国农业大学2115人才工程资助。
关键词
双时间尺度分解
温室环境
最优控制
模型预测控制
经济效益
two-time-scale decomposition
greenhouse climate
optimal control
model predictive control
profit
