中国农业大学学报2026，Vol.31Issue(2)：183-191,9.DOI:10.11841/j.issn.1007-4333.2026.02.16
双时间尺度温室环境最优控制仿真验证
A simulation validation for two-time-scale optimal control of greenhouse climate
摘要
Abstract
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.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Key words
two-time-scale decomposition/greenhouse climate/optimal control/model predictive control/profit分类
农业科技引用本文复制引用
徐丹,古卓朋,冉亚平,王书胜,徐雷,王明钦,马浚诚..双时间尺度温室环境最优控制仿真验证[J].中国农业大学学报,2026,31(2):183-191,9.基金项目
山东省重点研发计划(2022CXGC020708) （2022CXGC020708）
国家重点研发计划(2024YFD2000800) （2024YFD2000800）
国家自然科学基金项目(32371998) （32371998）
现代农业产业技术体系(CARS-23-D02) （CARS-23-D02）
北京市设施蔬菜创新团队项目(BAIC01-2025) （BAIC01-2025）
中国农业大学2115人才工程资助 （）
