深液流栽培试验温室温度系统的建模与控制
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摘要
温室环境控制是在充分利用自然资源的基础上,通过改变温室内部的温度、湿度、光照强度、CO_2浓度和作物根际的水分、营养组分等环境因子来获得作物生长的最佳条件,从而达到增加作物产量、改善品质、调节生产周期、提高经济效益的目的。温室小气候环境具有强非线性、大时滞、强耦合、强干扰、时变等特点,是一个非常复杂的动力学系统,而温室控制涉及多个学科的知识,包括作物生态生理学、园艺学、温室物理学、控制工程、仪器仪表和计算机科学。
温室小气候建模为温室控制提供了理论依据和决策支持。机理建模可以预测温室内环境,但是测量参数多、成本昂贵。基于输入输出数据的非线性模型系统辨识建模精度高,但是计算复杂,难以实时应用。基于输入输出数据的线性模型系统辨识大都没有结合机理模型考虑模型结构的合理性,以及栽培方式、气候特征、外部扰动对于模型的影响,建模精度不高。因此,研究综合考虑以上因素的温室小气候建模具有重要的理论及应用价值。
国内温室环境控制设备大多数为开关控制设备,控制设备没有位置反馈,无法使用国外的基于连续控制装备的温室建模和控制理论;采用常规的开关控制会导致设备的频繁开关,造成设备损耗和能源浪费。为了解决上述问题,本文将混杂系统建模与控制理论应用到温室环境的设备控制中。
本文的主要工作成果如下:
(1)将温室能量平衡方程与作物的蒸腾作用等生理学原理相结合,建立了深液流栽培条件下的试验温室温度系统的机理模型。仿真结果验证了机理模型的有效性。在此基础上对机理模型进行深入分析,获取试验建模的先验知识。
(2)研究了有智能监督级的试验温室温度系统的在线建模方法。针对我国气候环境、温室控制装备的特点,选用有外源输入的线性自回归滑动平均模型,根据机理建模所获得的先验知识,提出用统计假设检验法和拟合度分析相结合的方法确定模型结构,采用智能监督级保障辨识正常进行,并对残差进行白性检验。不同季节实测数据的仿真与预测表明:建模精度较高、计算速度快、抗干扰能力强,适合用于实时控制。
(3)研究了深液流栽培条件下网纹甜瓜和番茄的环境建模、管理与控制。给出了作物生长的温室小气候环境(温度、湿度、光照强度)和作物的根际环境(营养液的温度、EC值、pH值及主要离子浓度)的调控方法和实际的效果。证明了自主开发的基于CAN总线的温室环境与营养液测控系统的可靠性。
(4)研究了基于混合逻辑动态建模的温室温度系统的预测控制。引入辅助变量,将温室天窗开关动作、温度控制的约束条件以混合整数线性不等式表示,与温度系统的离散状态空间模型统一起来,分别建立了基于机理的混合逻辑动态模型和基于辨识的混合动态逻辑模型。在此基础上,研究了温室天窗温度系统混合动态逻辑模型的预测控制算法。结合温室实际管理经验,提出了天窗温度系统的性能指标。给出实验证明了模型的适用性与控制方法的合理性。
总结全文,提出温室小气候建模与控制领域需要进一步深入研究的问题。
The main tasks of greenhouse climate control are to make full use of natural resources and to control microclimate including temperature, humidity, solar radiation, concentration of CO_2, water and nutrient component of crop rhizosphere to create the optimum environment for the crops. It is one of the important tools for manipulating crop growth, increasing the yield and improving quality of greenhouse crop, regulating production cycle and improving economic benefit. Greenhouse microclimate has the characteristics of strong nonlinearity, large time delay, severe coupling, strong external noise disturbances, time-varying etc., so it's a very complex dynamics system. Greenhouse climate control has been dealt with traditionally by several disciplines: crop ecophysiology, horticulture, greenhouse physics, control engineering, instrument and computer science.
Greenhouse microclimate modes are essential for improving environment management and control efficiencies. Physical models can provide good predictions over short future time horizons, but they have the drawbacks of high cost and too many parameters. Non-linear models which are built by observing input and output data have good performances in simulation and prediction of greenhouse microclimate, but they require long computation time, which restricts their application to real-time implementation. At present, model structure, cultivation pattern, weather outside greenhouse, interference of the greenhouse growers and the like are not considered in linear models built by observing input and output data, which leads to low precision of models. Therefore, it is of theoretical and applicable importance for modeling of greenhouse microclimate.
Most of greenhouse climate control equipments in China are on-off control. There is no displacement feedback. So it is hard to use modeling and control theroy based on continuous control devices. The drawback of the conventional ON/OFF control algorithm is frequent switch actions of equipments, which leads to equipment spoilage and energy waste. To handle with the above problems, this dissertation mainly worked on modeling and control of air temperature system by deep flow technique nutrient solution based on hybrid system.
In this dissertation, the achievements are:
(1) The mechanism of greenhouse microclimate was analyzed utilizing laws of physics such as heat transfer, mass transfer, and the principles of crops physiology such as transpiration. Then modeling of the greenhouse inside temperature system by deep flow technique of nutrient solution was implemented according to energy-balance method. After that the mechanism models of the inside air temperature system were analyzed. Furthermore some prior information was obtained which was helpful to the subsequent experiment modeling research.
(2) Exploring whether on-line model could perform well and predict the temperature in an unheated, naturally ventilated greenhouse. Linear auto-regression moving average with extra input (ARMAX) model was used, and statistic hypothesis test, mechanism analysis and model fits analysis were used together to select the model structure. An intelligent supervisory segment was devised to monitor and fix problems appearing during the on-line modeling process. Moreover, the residuals passed white noise. Experiments were carried out in different seasons. It is concluded from various simulations that auto-regression with extra input (ARX) models produce better results than physical models and off-line models. Computation times for ARX models are much lower, so it's suitable for real-time implementation.
(3) Environment management and control of muskmelon and tomato cultivated by deep flow technique were studied. Different growth periods of the crop have different demands on the growing environment, comprising temperature, humidity, radiation and nutrient solution. According to different requirements, the greenhouse microclimate and roots environment of the crops (muskmelon and tomato) were controlled by the greenhouse measurement and control system developed by the authors. The results indicate that the circulatory system of nutrient solution and the measurement control system for greenhouse based on CAN bus is designed reasonably.
(4) Model predictive control of greenhouse temperature based on mixed logical dynamical systems was proposed. The auxiliary variables, the on-off statuses of the window and the constrained operations for temperature control were combined into the discrete-time state space model of temperature, thus the mixed logical dynamical system of greenhouse temperature was constructed. Mechanism models and identified models of the inside air temperature system were validated, respectively. Mixed logical dynamical systems with model predictive controllers were applied for window temperature system in a greenhouse. Quadratic index predictive control strategy was designed based on management experience. Simulations and experiments on the temperature control system have been made to validate the efficiency of the algorithms.
At the end of this thesis, all research work was summarized, and future research upon modeling and control of the greenhouse microclimate system was prospected.
温室小气候建模为温室控制提供了理论依据和决策支持。机理建模可以预测温室内环境,但是测量参数多、成本昂贵。基于输入输出数据的非线性模型系统辨识建模精度高,但是计算复杂,难以实时应用。基于输入输出数据的线性模型系统辨识大都没有结合机理模型考虑模型结构的合理性,以及栽培方式、气候特征、外部扰动对于模型的影响,建模精度不高。因此,研究综合考虑以上因素的温室小气候建模具有重要的理论及应用价值。
国内温室环境控制设备大多数为开关控制设备,控制设备没有位置反馈,无法使用国外的基于连续控制装备的温室建模和控制理论;采用常规的开关控制会导致设备的频繁开关,造成设备损耗和能源浪费。为了解决上述问题,本文将混杂系统建模与控制理论应用到温室环境的设备控制中。
本文的主要工作成果如下:
(1)将温室能量平衡方程与作物的蒸腾作用等生理学原理相结合,建立了深液流栽培条件下的试验温室温度系统的机理模型。仿真结果验证了机理模型的有效性。在此基础上对机理模型进行深入分析,获取试验建模的先验知识。
(2)研究了有智能监督级的试验温室温度系统的在线建模方法。针对我国气候环境、温室控制装备的特点,选用有外源输入的线性自回归滑动平均模型,根据机理建模所获得的先验知识,提出用统计假设检验法和拟合度分析相结合的方法确定模型结构,采用智能监督级保障辨识正常进行,并对残差进行白性检验。不同季节实测数据的仿真与预测表明:建模精度较高、计算速度快、抗干扰能力强,适合用于实时控制。
(3)研究了深液流栽培条件下网纹甜瓜和番茄的环境建模、管理与控制。给出了作物生长的温室小气候环境(温度、湿度、光照强度)和作物的根际环境(营养液的温度、EC值、pH值及主要离子浓度)的调控方法和实际的效果。证明了自主开发的基于CAN总线的温室环境与营养液测控系统的可靠性。
(4)研究了基于混合逻辑动态建模的温室温度系统的预测控制。引入辅助变量,将温室天窗开关动作、温度控制的约束条件以混合整数线性不等式表示,与温度系统的离散状态空间模型统一起来,分别建立了基于机理的混合逻辑动态模型和基于辨识的混合动态逻辑模型。在此基础上,研究了温室天窗温度系统混合动态逻辑模型的预测控制算法。结合温室实际管理经验,提出了天窗温度系统的性能指标。给出实验证明了模型的适用性与控制方法的合理性。
总结全文,提出温室小气候建模与控制领域需要进一步深入研究的问题。
The main tasks of greenhouse climate control are to make full use of natural resources and to control microclimate including temperature, humidity, solar radiation, concentration of CO_2, water and nutrient component of crop rhizosphere to create the optimum environment for the crops. It is one of the important tools for manipulating crop growth, increasing the yield and improving quality of greenhouse crop, regulating production cycle and improving economic benefit. Greenhouse microclimate has the characteristics of strong nonlinearity, large time delay, severe coupling, strong external noise disturbances, time-varying etc., so it's a very complex dynamics system. Greenhouse climate control has been dealt with traditionally by several disciplines: crop ecophysiology, horticulture, greenhouse physics, control engineering, instrument and computer science.
Greenhouse microclimate modes are essential for improving environment management and control efficiencies. Physical models can provide good predictions over short future time horizons, but they have the drawbacks of high cost and too many parameters. Non-linear models which are built by observing input and output data have good performances in simulation and prediction of greenhouse microclimate, but they require long computation time, which restricts their application to real-time implementation. At present, model structure, cultivation pattern, weather outside greenhouse, interference of the greenhouse growers and the like are not considered in linear models built by observing input and output data, which leads to low precision of models. Therefore, it is of theoretical and applicable importance for modeling of greenhouse microclimate.
Most of greenhouse climate control equipments in China are on-off control. There is no displacement feedback. So it is hard to use modeling and control theroy based on continuous control devices. The drawback of the conventional ON/OFF control algorithm is frequent switch actions of equipments, which leads to equipment spoilage and energy waste. To handle with the above problems, this dissertation mainly worked on modeling and control of air temperature system by deep flow technique nutrient solution based on hybrid system.
In this dissertation, the achievements are:
(1) The mechanism of greenhouse microclimate was analyzed utilizing laws of physics such as heat transfer, mass transfer, and the principles of crops physiology such as transpiration. Then modeling of the greenhouse inside temperature system by deep flow technique of nutrient solution was implemented according to energy-balance method. After that the mechanism models of the inside air temperature system were analyzed. Furthermore some prior information was obtained which was helpful to the subsequent experiment modeling research.
(2) Exploring whether on-line model could perform well and predict the temperature in an unheated, naturally ventilated greenhouse. Linear auto-regression moving average with extra input (ARMAX) model was used, and statistic hypothesis test, mechanism analysis and model fits analysis were used together to select the model structure. An intelligent supervisory segment was devised to monitor and fix problems appearing during the on-line modeling process. Moreover, the residuals passed white noise. Experiments were carried out in different seasons. It is concluded from various simulations that auto-regression with extra input (ARX) models produce better results than physical models and off-line models. Computation times for ARX models are much lower, so it's suitable for real-time implementation.
(3) Environment management and control of muskmelon and tomato cultivated by deep flow technique were studied. Different growth periods of the crop have different demands on the growing environment, comprising temperature, humidity, radiation and nutrient solution. According to different requirements, the greenhouse microclimate and roots environment of the crops (muskmelon and tomato) were controlled by the greenhouse measurement and control system developed by the authors. The results indicate that the circulatory system of nutrient solution and the measurement control system for greenhouse based on CAN bus is designed reasonably.
(4) Model predictive control of greenhouse temperature based on mixed logical dynamical systems was proposed. The auxiliary variables, the on-off statuses of the window and the constrained operations for temperature control were combined into the discrete-time state space model of temperature, thus the mixed logical dynamical system of greenhouse temperature was constructed. Mechanism models and identified models of the inside air temperature system were validated, respectively. Mixed logical dynamical systems with model predictive controllers were applied for window temperature system in a greenhouse. Quadratic index predictive control strategy was designed based on management experience. Simulations and experiments on the temperature control system have been made to validate the efficiency of the algorithms.
At the end of this thesis, all research work was summarized, and future research upon modeling and control of the greenhouse microclimate system was prospected.
引文
丁云花编著.2003.番茄无公害高效栽培[M].北京:金盾出版社.
马明建,李宝光,宋越东等.2003.基于直接推理的温室环境多变量模糊解耦控制[J].农业机械学报.34(6):116-119.
马立新编著.2003.温室番茄无公害栽培[M].北京:科学技术出版社.
方崇智,萧德云.1988.过程辨识[M].北京:清华大学出版社.
牛庆良,卜崇兴,黄丹枫.2005.网纹甜瓜基质栽培主要管理技术图解[J].农村实用工程技术.温室园艺.1:38-41.
王纪章,李萍萍,毛罕平,胡永光.2006.基于模型的温室环境调控技术研究[J].沈阳农业大学学报.37(3):463-466.
王定成.2004.温室环境的支持向量机回归建模[J].农业机械学报.35(5):106-109.
王定成.2003.支持向量机回归与控制的研究[D]:[博士].合肥:中国科学技术大学.
王子洋,秦琳琳,吴刚 等.2008.基于切换控制的温室温湿度控制系统建模与预测控制[J].农业工程学报.24(7):188-192.
王绍金,朱松明.1997.塑料大棚小气候的模拟和检测[J].农业工程学报,13(4):139-144.
邓玲黎,李百军,毛罕平.2004.长江中下游地区温室内温湿度预测模型的研究[J].农业工程学报.20(1):263-266.
邓璐娟,张侃谕,龚幼民,陈春宏.2005.温室环境多级控制系统及优化目标值设定的初步研究[J].农业工程学报.
邓禹贤编著.2002.新编无土栽培原理与技术[M].北京:中国农业出版社.
孙忠富,吴毅明,曹永华 等.1993.日光温室中直射光的计算机模拟方法:设施农业光环境模拟分析研究之三[J].农业工程学报.9(1):36-42.
孙忠富,李佑祥,吴毅明等.1993.北京地区典型日光温室直射光环境的模拟与分析:设施农业光环境模拟分析研究之四[J].农业工程学报.9(2):45-51.
司慧萍,苗香雯.2005.华东型连栋塑料温室自然通风开窗度模型及试验研究[J].浙江大学 学报(农业与生命科学版).31(1):113-118.
冯培悌.2004.系统辨识[M].杭州:浙江大学出版社(第二版).
李晋.2007.试验温室温湿度系统建模研究.[D]:[硕士].合肥:中国科学技术大学.
李晋,秦琳琳,岳大志 等.2008a.试验温室温度系统建模与仿真[J].系统仿真学报.20(7):1869-1875.
李晋,秦琳琳,吴刚 等.2008b.基于加权最小二乘支持向量机的温室小气候建模与仿真[J].系统仿真学报,2008,20(16).
李秀梅,赵春江,乔晓军 等.2004.基于改进遗传算法的温湿度模糊神经网络控制器[J].农业工程学报.20(1):259-262.
李秀改.高东杰.2002.一类混杂系统的模型预测控制[J].信息与控制.31(1):1-4.
李秀改.2003.基于混合逻辑动态的混杂系统建模和控制方法研究[D]:[博士].北京:中国科学院自动化研究所.
李元哲,吴德让,于竹.1994.日光温室微气候的模拟与实验研究[J].农业工程学报10(1):130-136.
李树海,马承伟,张俊芳 等.2004.多层覆盖连栋温室热环境模型构建[J].农业工程学报.20(3):217-221.
祁睿,秦琳琳,薛美盛 等.2005.基于CAN总线的温室监控系统设计与应用[J].工业仪表与自动化装置.3:17-20.
刘士哲.2001.现代实用无土栽培技术[M].北京:中国农业出版社.
刘声锋,郭文忠,冯志红 等.2003.银川地区甜瓜滴灌节水栽培技术[J].上海蔬菜.6:45-46.
许超,陈治纲,邵惠鹤.2002.预测控制技术及应用发展综述[J].化工自动化及仪表.29(3):1-10.
孙明玮,陈增强.2000.无限时域预测控制的直接求解方法[J].应用数学.13(3):5-9.
宋卫堂,黄之栋,张树阁.2003a.番茄单株高产优质营养液栽培技术[J].生物学通报.38(4):3-5.
宋卫堂,张树阁,黄之栋.2003b.番茄单株高产优质营养液栽培技术[J],中国农学通报.19(1):5-8.
宋卫堂,高丽红,张树阁 等.2005.深液流栽培番茄根际氧环境的试验研究[J].中国农学通报.21(1):219-223.
宋崇辉,柴天佑.2004.一类具有稳定性的广义预测控制算法[J].自动化学报.30(6): 807-815.
吴刚.2003.预测控制讲义[M/OL].中国科学技术大学.ftp://202.38.73.141/lab/teachers/wug/.
张华,刘钧如,黄亮华 等.1995.黄瓜深液流周年无土栽培实验[J].长江蔬菜.2:35-36.
杜尚丰,王一鸣,马承伟 等.2003.温室环境温度智能控制算法研究[J].计算机测量与控制.11(11):850-852.
佟国红,李保明,David M.Christopher等.2007.用CFD方法模拟日光温室温度环境初探[J],农业工程学报.23(7):178-185.
陈青云,汪政富.1996.节能型日光温室热环境的动态模拟[J].中国农业大学学报.1(1):67-72.
陈端生,郑海山,张建国 等.1992.日光温室气象环境综合研究(三):几种弧型采光屋面温室内直射光量的比较研究[J].农业工程学报.8(4):78-82.
陈薇,秦琳琳,吴刚等.2007.硝酸根离子选择电极建模[J].传感技术学报.20(1):14-17.
沈明卫,苗香雯,崔绍荣.2000.华东型连栋塑料温室室内光环境的研究[J].浙江大学学报,26(5):533-536.
沈维道,蒋智敏,童钧耕.2000.工程热力学[M].北京:高等教育出版社.
汪小旵,罗卫红,丁为民 等.2002.南方现代化温室黄瓜夏季蒸腾研究[J].中国农业科学.35(11):1390-1395.
汪小旵.2003.南方现代化温室小气候模拟及其能耗预测研究[D]:[博士].南京:南京农业大学.
杨世铭,陶文铨.2006.传热学[M].北京:高等教育出版社.
罗卫红,汪小旵,戴剑锋 等.2004.南方现代化温室黄瓜冬季蒸腾测量与模拟研究[J].植物生态学报.28(1):59-65.
罗华平.2004.亚热带叶菜深液流栽培试验初报[J].广西农业科学.1:42-43.
罗汉民,吴诗敦,谭克光编.1980.气候学[M].北京:气象出版社.
周长吉,杨振声,冯广和编.2002.现代温室工程[M].北京:化学工业出版社.
庞国仲 编.1998.自动控制原理[M].第2版.合肥:中国科学技术大学出版社.
郑大钟,赵千川.2000.离散事件动态系统[M].北京:清华大学出版社.
尚书旗,董佑福,史岩主编.设施栽培工程技术[M].北京:中国农业出版社.
郦伟,董仁杰,汤楚宙.1997.日光温室的热环境理论模型[J].农业工程学报.13(2):160-163.
席裕庚.1993.预测控制[M].北京:国防工业出版社.
郜庆炉,薛香,段爱旺.2003.日光温室内温度特点及其变化规律研究[J].灌溉排水学.22(6):50-53.
莫以为,萧德云.2002.混合动态系统及应用综述[J].控制理论与应用.19(1):1-8
秦琳琳,孙德敏,王永 等.2003.无土栽培营养液循环控制系统[J].农业工程学报.19(4):264-266.
秦琳琳,吴刚,薛美盛 等.2007a.网纹甜瓜营养液深液流栽培管理与环境调控[J].中国科学技术大学学报.37(2):195-201.
秦琳琳,吴刚,薛美盛 等.2007b.基于CAN现场总线的温室环境检测控制系统的开发[J].现代农业理论与实践(安徽农业现代农业博士科技论坛文集).合肥:安徽大学出版,457-460.
顾寄南,毛罕平.2001.温室环境智能化控制数学模型的研究[J].农业机械学报.32(6):63-65.
夏天长.1983.系统辨识最小二乘法[M].北京:清华大学出版社(第一版).
曹卫星,罗卫红.2000.作物系统模拟及智能管理[M].北京:华文出版社.
黄湘云,朱学峰.2005.预测控制的研究现状与展望[J].石油化工自动化.2005年第2期:27-31.
程曙,张浩.2005.基于混合逻辑动态制造系统建模方法研究[J].计算机工程及应用.32:204-209.
曾锋,高东杰.2006.基于混合逻辑动态的混杂系统研究及应用[J].控制工程.13(1):60-65.
梅家训主编.2002.工厂化蔬菜生产[M].北京:中国农业出版社.
薛美盛,胡振华,秦琳琳等.2006.基于CAN总线的温室可控环境综合测控系统软件设计[J].测控技术.25(10):61-64.
戴剑锋.2006.基于模型的温室加温目标设定系统的研究[D]:[博士].南京:南京农业大学.
戴剑锋,罗卫红,乔晓军 等.2006.基于模型的温室加温控制目标设定系统的研究[J].农业工程学报.22(11):187-191.
Alberto Bemporad, Giancarlo Ferrari-Trecate, and Manfred Morari. 2000. Observability and Controllability of Piecewise Affine and Hybrid Systems. IEEE transactions on Automatic control. 45(10): 1864:1876.
Albert Setiawan, Louis D. Albright, Richard M. Phelan. 2000. Application of pseudo-derivative-feedback algorithm in greenhouse air temperature control[J] . Computers and Electronics in Agriculture. 26 : 283-302.
Alberto Bemporad, Manfred Morari. 1999. Control of systems integrating logic, dynamics, and constraints [J]. Automatica. 35: 407-427.
Alberto Bemporad, W. P. Maurice H. Heemels, Bart De Schutter. 2002. On Hybrid Systems and Closed-Loop MPC Systems[J].IEEE Transaction on Automatic Control. 47(5):863-869.
Alberto Pardossi, Fernando Malorgio. 2002. A comparison between two methods to control nutrient delivery to greenhouse melon grown in recirculating nutrient solution culture[J].Scientia Horticulturae. 92:89-95.
Antsaklis P J, Stiver J A, Lemmon M D. 1993. Hybrid system modeling and autonomous control systems [A]. In Grossman R L, et al(Eds.). Hybrid Systems[M]. New York , USA:Springer-Verlag. 366-392.
A.J. Udink ten Cate. 1985. Modelling and simulation in greenhouse climate control[J]. Acta Horticulturae. 174:461-467.
Alur R, Courcouberies C, Henzinger T A, et al. 1993. Hybrid automata: An algorithmic approach to the specification and verification of hybrids systems[A]. In Grossman R L, et al (Eds.).Hybrid Systems[M]. New York, USA: Springer-Verlag. 209-229.
Alur R, David D. 1994. The theory of timed automata[J]. Theoretical and Computer Science.126(2): 183-235.
B Bailey, J Montero, C Biel, et al. 1993. Transpiration of Ficus Benjamin: comparison of measurements with predictions of the Penman-Monteith model and a simplified version[J].Agricultural and Forest Meteorology. 65: 229-243.
Bemporad A, Mignone D and Morari M. 1999. Moving horizon estimation for hybrid systems and fault detection [A]. In Proceedings of ACC'99 [C]. San Diego, CA, USA. 2471- 2475.
Bemporad A, Torrisi F D and Morari M. 1999. Optimization - based verification and stability characterization of piecewise affine and hybrid systems [R]. Switzerland: Automatic Control Lab, ETH Zurich, Tech. Report AUT99 - 17.
Bemporad A, Morari M. 1999. Control of systems integrating logic, dynamics, and constraints [J].Automatica. 35:407-427.
Bemporad A, Morari M, Dua V, et al. 2002. The explicit linear quadratic regulator for constrained systems [J]. Automatica. 38(1):3 -20.
B J Bailey. 2000. Constraints, limitations and achievements in greenhouse natural ventilation [J].Acta Horticulturae. 534: 21-30.
Boulard T, Wang S. 2000. Greenhouse crop transpiration simulation from external climate conditions[J].Agricultural and Forest Meteorology. 100: 25-34.
Branicky M S. 1998. Multiple Lyapunov functions and analysis tools for switched and hybrid systems [J]. IEEE Transactions on Automatic Control. 34(4): 475 -482.
Carlo Macca. 2004. Response time of ion-selective electrodes Current usage versus IUPAC recommendations[J]. Analytica Chimica Acta. 512: 183-190.
Cecilia Stanghellini, Taeke de Jong. 1995. A model of humidity and its applications in a greenhouse. Agricultural and Forest Meteorology. 76 :129-148.
Deniel LiberZo. 2003. Switching in System and Control, System & Control:Foundations&Applications [M]. Boston.: Birkhauser.
De Jong T. 1990. Natural ventilation of large multispan greenhouses[D]: [Ph. D.]. Netherlands :Agricultural University, Wageningen.
Demongodin I, Koussoulas N T. 1998. Differential Petri nets: Representin continuous system in a discrete-event world [J]. IEEE Transaction on Automatic Control. 34(4):573-579.
De Zwart H F. 1993. Determination of direct transmission of a multi-span greenhouse using vector algebra [J]. Journal of Agricultural Engineering Research. 56: 39-49.
De Zwart H F. 1996. Analyzing energy-saving options in greenhouse cultivation using a simulation model [D]: [Ph. D.]. Netherlands: University of Wageningen.
E. D. Sontag. 1981. Nonlinear regulation: The piecewise linear approach[J]. IEEE Transactions on Automation Control. 26: 346-358.
Fabio Danilo Torrisi. Alberto Bemporad. 2004. HYSDEL—A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems. IEEE Transactions on control systems technology[J]. 12(2): 235-249.
Fathi Fourati, Mohamed Chtourou. 2007. A greenhouse control with feed-forward and recurrent neural networks[J]. Simulation Modelling Practice and Theory .15: 1016-1028.
Francesco Borrelli, Alberto Bemporad, Michael Fodor and Davor Hrovat. 2006. An MPC/Hybird sytem approach to traction control. IEEE transactions on control systems technology.14(3):541-552.
G.D. Pasgianos, K.G. Arvanitis, P. Polycarpou, N. Sigrimis. 2003. A nonlinear feedback technique for greenhouse environmental control[J]. Computers and Electronics in Agriculture 40 :153-177.
GP.A.Bot. 1983. Greenhouse climate: from physical processes to a dynamic model [D]: [Ph. D.].Netherlands : Agricultural University, Wageningen.
G. Van Straten, H. Challa, and F. Buwalda. 2000. Towards user accepted optimal control of greenhouse climate[J]. Comput. Electron. Agriculture. 26: 221-238.
H.Uchida Frausto, J. P. Pieters, J. M. Deltour. 2003. Modelling Greenhouse Temperature by means of auto regressive models[J]. Biosystems Engineering. 84(2): 147-157.
http://e-nw.shac.gov.cn/wmfw/hwzc/hwkj/200804/t20080414_336903.htm.
http://www.hfqx..com.cn/.
http://www.jlcbs.gov.cn/html/11/26/26-4078.html
He K X , Lemmon M D. 1998. Lyapunov stability of continuous- valued systems under the supervision of discrete- event transition Systems [A]. In Henzinger T A, Sastry S ( Eds.).Hybrid Systems: Computation and Control (HSCC'98)[M]. Berlin,Germany:Springer-Verlag : 175-189.
I.Seginer, T. Boulard, B.J. Bailey. 1994. Neural network models of the greenhouse climate[J].Agric. Eng. Res. 59: 203-216.
I.Seginer. 1997. Some artificial neural network applications to greenhouse environmental control[J]. Comput. Electron. Agriculture. 18: 167-186.
I. Laribi, H. Homri, R.Mlhiri. 2006. Modeling of a greenhouse temperature : comparison between multimodel and neural approaches[J]. 7:99-404
J. Boaventura Cunha, C. Couto, and A.E. Ruano. 1997. Real-time parameter estimation of dynamic temperature models for greenhouse environmental control[J]. Control Eng. Practice .5(10): 1473-1481.
J. Boaventura Cunha. 2003. Greenhouse climate models: an overview[C]. EFITA 2003 Conference.Debrecen, Hungary. July 5-9: 823-829.
J.C.Bakker, GRA.Bot, H.Challa, N.J.Van de Braak. 1995. Geeenhouse Climate Control[M].Wageningen: Wageningen Pers.
Jitendra K. Tugnait. 1994. Linear model validation and order selection using higher-order statistics[J]. IEEE Transactions on Signal Processing. 42:1728-1736.
J. Litago, F.J. Baptista, J.F. Meneses et al. 2005. Statistical modelling of the microclimate in a naturally ventilated greenhouse. Biosystems Engineering. 92 (3): 365 - 381.
J.L Villa, A.Gauthier and N. Rakoto-Ravalontsalama. 2005. Model predictive control of mld models with intergrators[J]. Proceedings of the 2005 IEEE conference on control applications.Toronto, Canada, 641-644.
J.M. Herrero, X. Blasco, M. Martinez. et al. 2007. Non-linear robust identification of a greenhouse model using multi-objective evolutionary algorithms[J]. Biosystems engineering.98:335-346.
Johansson M and Rantzer A. 1998. Computation of piecewise quadratic Lyapunov function for hybrid systems [J]. IEEE Transaction on Automatic Control. 34(4) : 555-559.
J.P. Coelho, P.B. de Moura Oliveira, J. Boaventura Cunha. 2005. Greenhouse air temperature predictive control using the particle swarm optimisation algorithm[J]. Computers and Electronics in Agriculture. 49:330-344.
Kupers.G., van Gaalen. J., Gieling. T.h.H. and van Os, E.A. 1992. Diural Changes in the Ion Concentration of the Supply and Return Water of A Tomato Crop Grown on Rockwool[J].Acta Hort.304:291-300.
Ljung L. 2002. System Identification for the user[M]. Peking: Tsinghua University Press & Prentice Hall PTR.
Marziyeh Keshavarz, Mojtaba Barkhordary and Mohammad Reza Jahed Motlagh. 2007. Mixed logic dynamical modeling and control of steam boiler[J]. International conference on control,Automation and systems. COEX, Seoul, Korea. 299-304.
Mayne D Q, Rawlings J B, Rao C V, Scokaert POM. 2000. Constrained model predictive control:stability and optimality[J]. Automatica. 36(4): 789-814.
M Bailie, A Bailie, D Delmon. 1994. Microclimate and transpiration of greenhouse rose crops[J].Agricultural and Forest Meteorology. 71: 83-97.
M. Monsi, T. Saeki.Uber den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung fur die Stoffproduktion[J]. The Journal of Japanese Botany. 14: 22-52.
Morimoto. T., Nishina. H., Hashimoto. Y and Watake. H. 1992. Sensor for Ion-Control:An Approach to Control of Nutrient Solution in Hydroponics. Acta Hort. 304:301-308.
Morari M, Lee J H, 1999. Model predictive control: Past, present and future [J].Computers and Chemical Engineering. 23: 667-682.
M.S. Branicky, V.S. Borkar, and S.K. Mitter. 1998. A united framework for hybrid control: model and optimal control theory. IEEE Transactions on Automatic Control. 43(1):31-45.
M.Y.E1 Ghoumari, H-J.Tantau and J.Serrano. 2005. Nonlinear constrained MPC: real-time implementation of greenhouse air temperature control. Computers and Electronics in Agriculture. 49 : 345-356.
N. Sigrimis, N. Rerras. 1996. A linear model for greenhouse control[J]. Trans. ASAE. 39 (1):253-261.
Nielsen B, Madsen H. 1996. Identification of transfer functions for control of greenhouse air temperature. Journal of Agricultural Engineering Research. 60: 25-34.
N. Bennis, J. Duplaix, G. Enea, M. Halouac, H. Youlal, et al. 2008. Greenhouse climate modelling and robust control[J]. Computers and electronics in agriculture. 96-107
O.Kornera, G. Van Straten. 2007. Decision support for dynamic greenhouse climate control strategies[J]. Computers and electronics in agriculture. 1-13.
Peter J.Brockwell, Richard A.Davis. 2003. Time Series: Theory and Methods (3nd Edition)[M],New York: Springer-Verlag Inc.
Pettersson S. 1999. Analysis and design of hybrid systems [D] : [Ph. D.]. Goteborg, Sweden :Chalmers University of Technology.
Philip J.Smethurst. 2000. Soil solution and other soil analyses as indicators of nutrient supply: a review[J]. Forest ecology and management 138:397-411.
Rossiter J A, Gossner J R, Kouvaritakis B. 1996. Infinite horizon stable predictive control[J].IEEE Transactions on Automatic Control. 41(10): 1522-1527.
S.L. Patil, H.J. Tantau, V.M. Salokhe. 2008. Modelling of tropical greenhouse temperature by auto regressive and neural network models[J]. Biosystems Engineering. 99: 423 - 431.
S. Pinon , E.F. Camachoa, B. Kuchen, M. Pena. 2005. Constrained predictive control of a greenhouse[J]. Computers and Electronics in Agriculture. 49:317-329.
T. Boulard, A. Bailie. 1995. Modelling of air exchange in a greenhouse equipped with continuous roof vents[J]. Journal of Agricultural Engineering Research. 61:37-48.
T. Boulard, P. Feuilloley, C. Kittas.Natural. 1997. Ventilation performance of six greenhouse and tunnel types[J]. Journal of Agricultural Engineering Research. 67:249-266.
Torrisi F D, Bemporad A. 2004. HYSDEL-A tool for generating computational hybrid models[J].IEEE Trans on Control Systems Technology. 12(2):235-249.
Tromp L, Benveniste A, Basseville M. 1999. Fault detection and isolation in hybrid systems: A Petri- net approach [A]. In 14th IFAC World Congress [C], Beijing. 79 - 84.
Uchida Frausto H, Pieters J G 2004. Modelling greenhouse temperature using system identification by means of neural networks[J]. Neuro computing. 56(1): 423-428.
Van den Vlekkert, H.H., Kouwenhoven, J.P.M., van Wingerden. 1992. A.A.M. Application of ISFETS in Closed-Loop Systems for Horticulature[J]. Acta Hort. (ISHS). 304:309-320.
V. D. Blondel, J. N. Tsitsiklis. 1999. Complexity of stability and controllability of elementary hybrid systems[J]. Automatica. 35:479-489.
Weihong Luo, Hentrik Feije, de Zwart, et al.. 2005. Simulation of Greenhouse Management in the Subtropics, Part I-Model Validation and Scenario Study for the Winter Season[J]. Biosystems Engineering. 90(3): 307-318.
Weihong Luo, Cecilia Stanghellini, et al.. 2005. Simulation of Greenhouse Management in the Subtropics, Part H-Scenario Study for the Summer Season, Biosystems Engineering, 90(4):433-441.
Witsenhausen,H, 1966. A class of hybrid-state continuous-timedynamic systems[J]. IEEE Transactions on. Automatic control. 11(2): 161-167.
Wu Gang, Zhang Zhigang, Zhang Yuzhu and Sun Demin. 1994. Adaptive PID Control Software Based on Intelligent Supervisor and Its Application to an Acrylonitrile Plant[J]. Chinese Journal of Automation. 6(2):125-130.
Ye H, Michel A and Hou L . 1998. Stability Theory for hybrid dynamical systems [J]. IEEE Transctions on Automatic Control. 34 (4): 461-474.
Zhou Ji, Zhang Yingqi.2003. Influence of Surface Active Agent on the Response Time of Chloride Ion Selective Electrode[J]. Chemical Sensor. 23(3):62-65.
马明建,李宝光,宋越东等.2003.基于直接推理的温室环境多变量模糊解耦控制[J].农业机械学报.34(6):116-119.
马立新编著.2003.温室番茄无公害栽培[M].北京:科学技术出版社.
方崇智,萧德云.1988.过程辨识[M].北京:清华大学出版社.
牛庆良,卜崇兴,黄丹枫.2005.网纹甜瓜基质栽培主要管理技术图解[J].农村实用工程技术.温室园艺.1:38-41.
王纪章,李萍萍,毛罕平,胡永光.2006.基于模型的温室环境调控技术研究[J].沈阳农业大学学报.37(3):463-466.
王定成.2004.温室环境的支持向量机回归建模[J].农业机械学报.35(5):106-109.
王定成.2003.支持向量机回归与控制的研究[D]:[博士].合肥:中国科学技术大学.
王子洋,秦琳琳,吴刚 等.2008.基于切换控制的温室温湿度控制系统建模与预测控制[J].农业工程学报.24(7):188-192.
王绍金,朱松明.1997.塑料大棚小气候的模拟和检测[J].农业工程学报,13(4):139-144.
邓玲黎,李百军,毛罕平.2004.长江中下游地区温室内温湿度预测模型的研究[J].农业工程学报.20(1):263-266.
邓璐娟,张侃谕,龚幼民,陈春宏.2005.温室环境多级控制系统及优化目标值设定的初步研究[J].农业工程学报.
邓禹贤编著.2002.新编无土栽培原理与技术[M].北京:中国农业出版社.
孙忠富,吴毅明,曹永华 等.1993.日光温室中直射光的计算机模拟方法:设施农业光环境模拟分析研究之三[J].农业工程学报.9(1):36-42.
孙忠富,李佑祥,吴毅明等.1993.北京地区典型日光温室直射光环境的模拟与分析:设施农业光环境模拟分析研究之四[J].农业工程学报.9(2):45-51.
司慧萍,苗香雯.2005.华东型连栋塑料温室自然通风开窗度模型及试验研究[J].浙江大学 学报(农业与生命科学版).31(1):113-118.
冯培悌.2004.系统辨识[M].杭州:浙江大学出版社(第二版).
李晋.2007.试验温室温湿度系统建模研究.[D]:[硕士].合肥:中国科学技术大学.
李晋,秦琳琳,岳大志 等.2008a.试验温室温度系统建模与仿真[J].系统仿真学报.20(7):1869-1875.
李晋,秦琳琳,吴刚 等.2008b.基于加权最小二乘支持向量机的温室小气候建模与仿真[J].系统仿真学报,2008,20(16).
李秀梅,赵春江,乔晓军 等.2004.基于改进遗传算法的温湿度模糊神经网络控制器[J].农业工程学报.20(1):259-262.
李秀改.高东杰.2002.一类混杂系统的模型预测控制[J].信息与控制.31(1):1-4.
李秀改.2003.基于混合逻辑动态的混杂系统建模和控制方法研究[D]:[博士].北京:中国科学院自动化研究所.
李元哲,吴德让,于竹.1994.日光温室微气候的模拟与实验研究[J].农业工程学报10(1):130-136.
李树海,马承伟,张俊芳 等.2004.多层覆盖连栋温室热环境模型构建[J].农业工程学报.20(3):217-221.
祁睿,秦琳琳,薛美盛 等.2005.基于CAN总线的温室监控系统设计与应用[J].工业仪表与自动化装置.3:17-20.
刘士哲.2001.现代实用无土栽培技术[M].北京:中国农业出版社.
刘声锋,郭文忠,冯志红 等.2003.银川地区甜瓜滴灌节水栽培技术[J].上海蔬菜.6:45-46.
许超,陈治纲,邵惠鹤.2002.预测控制技术及应用发展综述[J].化工自动化及仪表.29(3):1-10.
孙明玮,陈增强.2000.无限时域预测控制的直接求解方法[J].应用数学.13(3):5-9.
宋卫堂,黄之栋,张树阁.2003a.番茄单株高产优质营养液栽培技术[J].生物学通报.38(4):3-5.
宋卫堂,张树阁,黄之栋.2003b.番茄单株高产优质营养液栽培技术[J],中国农学通报.19(1):5-8.
宋卫堂,高丽红,张树阁 等.2005.深液流栽培番茄根际氧环境的试验研究[J].中国农学通报.21(1):219-223.
宋崇辉,柴天佑.2004.一类具有稳定性的广义预测控制算法[J].自动化学报.30(6): 807-815.
吴刚.2003.预测控制讲义[M/OL].中国科学技术大学.ftp://202.38.73.141/lab/teachers/wug/.
张华,刘钧如,黄亮华 等.1995.黄瓜深液流周年无土栽培实验[J].长江蔬菜.2:35-36.
杜尚丰,王一鸣,马承伟 等.2003.温室环境温度智能控制算法研究[J].计算机测量与控制.11(11):850-852.
佟国红,李保明,David M.Christopher等.2007.用CFD方法模拟日光温室温度环境初探[J],农业工程学报.23(7):178-185.
陈青云,汪政富.1996.节能型日光温室热环境的动态模拟[J].中国农业大学学报.1(1):67-72.
陈端生,郑海山,张建国 等.1992.日光温室气象环境综合研究(三):几种弧型采光屋面温室内直射光量的比较研究[J].农业工程学报.8(4):78-82.
陈薇,秦琳琳,吴刚等.2007.硝酸根离子选择电极建模[J].传感技术学报.20(1):14-17.
沈明卫,苗香雯,崔绍荣.2000.华东型连栋塑料温室室内光环境的研究[J].浙江大学学报,26(5):533-536.
沈维道,蒋智敏,童钧耕.2000.工程热力学[M].北京:高等教育出版社.
汪小旵,罗卫红,丁为民 等.2002.南方现代化温室黄瓜夏季蒸腾研究[J].中国农业科学.35(11):1390-1395.
汪小旵.2003.南方现代化温室小气候模拟及其能耗预测研究[D]:[博士].南京:南京农业大学.
杨世铭,陶文铨.2006.传热学[M].北京:高等教育出版社.
罗卫红,汪小旵,戴剑锋 等.2004.南方现代化温室黄瓜冬季蒸腾测量与模拟研究[J].植物生态学报.28(1):59-65.
罗华平.2004.亚热带叶菜深液流栽培试验初报[J].广西农业科学.1:42-43.
罗汉民,吴诗敦,谭克光编.1980.气候学[M].北京:气象出版社.
周长吉,杨振声,冯广和编.2002.现代温室工程[M].北京:化学工业出版社.
庞国仲 编.1998.自动控制原理[M].第2版.合肥:中国科学技术大学出版社.
郑大钟,赵千川.2000.离散事件动态系统[M].北京:清华大学出版社.
尚书旗,董佑福,史岩主编.设施栽培工程技术[M].北京:中国农业出版社.
郦伟,董仁杰,汤楚宙.1997.日光温室的热环境理论模型[J].农业工程学报.13(2):160-163.
席裕庚.1993.预测控制[M].北京:国防工业出版社.
郜庆炉,薛香,段爱旺.2003.日光温室内温度特点及其变化规律研究[J].灌溉排水学.22(6):50-53.
莫以为,萧德云.2002.混合动态系统及应用综述[J].控制理论与应用.19(1):1-8
秦琳琳,孙德敏,王永 等.2003.无土栽培营养液循环控制系统[J].农业工程学报.19(4):264-266.
秦琳琳,吴刚,薛美盛 等.2007a.网纹甜瓜营养液深液流栽培管理与环境调控[J].中国科学技术大学学报.37(2):195-201.
秦琳琳,吴刚,薛美盛 等.2007b.基于CAN现场总线的温室环境检测控制系统的开发[J].现代农业理论与实践(安徽农业现代农业博士科技论坛文集).合肥:安徽大学出版,457-460.
顾寄南,毛罕平.2001.温室环境智能化控制数学模型的研究[J].农业机械学报.32(6):63-65.
夏天长.1983.系统辨识最小二乘法[M].北京:清华大学出版社(第一版).
曹卫星,罗卫红.2000.作物系统模拟及智能管理[M].北京:华文出版社.
黄湘云,朱学峰.2005.预测控制的研究现状与展望[J].石油化工自动化.2005年第2期:27-31.
程曙,张浩.2005.基于混合逻辑动态制造系统建模方法研究[J].计算机工程及应用.32:204-209.
曾锋,高东杰.2006.基于混合逻辑动态的混杂系统研究及应用[J].控制工程.13(1):60-65.
梅家训主编.2002.工厂化蔬菜生产[M].北京:中国农业出版社.
薛美盛,胡振华,秦琳琳等.2006.基于CAN总线的温室可控环境综合测控系统软件设计[J].测控技术.25(10):61-64.
戴剑锋.2006.基于模型的温室加温目标设定系统的研究[D]:[博士].南京:南京农业大学.
戴剑锋,罗卫红,乔晓军 等.2006.基于模型的温室加温控制目标设定系统的研究[J].农业工程学报.22(11):187-191.
Alberto Bemporad, Giancarlo Ferrari-Trecate, and Manfred Morari. 2000. Observability and Controllability of Piecewise Affine and Hybrid Systems. IEEE transactions on Automatic control. 45(10): 1864:1876.
Albert Setiawan, Louis D. Albright, Richard M. Phelan. 2000. Application of pseudo-derivative-feedback algorithm in greenhouse air temperature control[J] . Computers and Electronics in Agriculture. 26 : 283-302.
Alberto Bemporad, Manfred Morari. 1999. Control of systems integrating logic, dynamics, and constraints [J]. Automatica. 35: 407-427.
Alberto Bemporad, W. P. Maurice H. Heemels, Bart De Schutter. 2002. On Hybrid Systems and Closed-Loop MPC Systems[J].IEEE Transaction on Automatic Control. 47(5):863-869.
Alberto Pardossi, Fernando Malorgio. 2002. A comparison between two methods to control nutrient delivery to greenhouse melon grown in recirculating nutrient solution culture[J].Scientia Horticulturae. 92:89-95.
Antsaklis P J, Stiver J A, Lemmon M D. 1993. Hybrid system modeling and autonomous control systems [A]. In Grossman R L, et al(Eds.). Hybrid Systems[M]. New York , USA:Springer-Verlag. 366-392.
A.J. Udink ten Cate. 1985. Modelling and simulation in greenhouse climate control[J]. Acta Horticulturae. 174:461-467.
Alur R, Courcouberies C, Henzinger T A, et al. 1993. Hybrid automata: An algorithmic approach to the specification and verification of hybrids systems[A]. In Grossman R L, et al (Eds.).Hybrid Systems[M]. New York, USA: Springer-Verlag. 209-229.
Alur R, David D. 1994. The theory of timed automata[J]. Theoretical and Computer Science.126(2): 183-235.
B Bailey, J Montero, C Biel, et al. 1993. Transpiration of Ficus Benjamin: comparison of measurements with predictions of the Penman-Monteith model and a simplified version[J].Agricultural and Forest Meteorology. 65: 229-243.
Bemporad A, Mignone D and Morari M. 1999. Moving horizon estimation for hybrid systems and fault detection [A]. In Proceedings of ACC'99 [C]. San Diego, CA, USA. 2471- 2475.
Bemporad A, Torrisi F D and Morari M. 1999. Optimization - based verification and stability characterization of piecewise affine and hybrid systems [R]. Switzerland: Automatic Control Lab, ETH Zurich, Tech. Report AUT99 - 17.
Bemporad A, Morari M. 1999. Control of systems integrating logic, dynamics, and constraints [J].Automatica. 35:407-427.
Bemporad A, Morari M, Dua V, et al. 2002. The explicit linear quadratic regulator for constrained systems [J]. Automatica. 38(1):3 -20.
B J Bailey. 2000. Constraints, limitations and achievements in greenhouse natural ventilation [J].Acta Horticulturae. 534: 21-30.
Boulard T, Wang S. 2000. Greenhouse crop transpiration simulation from external climate conditions[J].Agricultural and Forest Meteorology. 100: 25-34.
Branicky M S. 1998. Multiple Lyapunov functions and analysis tools for switched and hybrid systems [J]. IEEE Transactions on Automatic Control. 34(4): 475 -482.
Carlo Macca. 2004. Response time of ion-selective electrodes Current usage versus IUPAC recommendations[J]. Analytica Chimica Acta. 512: 183-190.
Cecilia Stanghellini, Taeke de Jong. 1995. A model of humidity and its applications in a greenhouse. Agricultural and Forest Meteorology. 76 :129-148.
Deniel LiberZo. 2003. Switching in System and Control, System & Control:Foundations&Applications [M]. Boston.: Birkhauser.
De Jong T. 1990. Natural ventilation of large multispan greenhouses[D]: [Ph. D.]. Netherlands :Agricultural University, Wageningen.
Demongodin I, Koussoulas N T. 1998. Differential Petri nets: Representin continuous system in a discrete-event world [J]. IEEE Transaction on Automatic Control. 34(4):573-579.
De Zwart H F. 1993. Determination of direct transmission of a multi-span greenhouse using vector algebra [J]. Journal of Agricultural Engineering Research. 56: 39-49.
De Zwart H F. 1996. Analyzing energy-saving options in greenhouse cultivation using a simulation model [D]: [Ph. D.]. Netherlands: University of Wageningen.
E. D. Sontag. 1981. Nonlinear regulation: The piecewise linear approach[J]. IEEE Transactions on Automation Control. 26: 346-358.
Fabio Danilo Torrisi. Alberto Bemporad. 2004. HYSDEL—A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems. IEEE Transactions on control systems technology[J]. 12(2): 235-249.
Fathi Fourati, Mohamed Chtourou. 2007. A greenhouse control with feed-forward and recurrent neural networks[J]. Simulation Modelling Practice and Theory .15: 1016-1028.
Francesco Borrelli, Alberto Bemporad, Michael Fodor and Davor Hrovat. 2006. An MPC/Hybird sytem approach to traction control. IEEE transactions on control systems technology.14(3):541-552.
G.D. Pasgianos, K.G. Arvanitis, P. Polycarpou, N. Sigrimis. 2003. A nonlinear feedback technique for greenhouse environmental control[J]. Computers and Electronics in Agriculture 40 :153-177.
GP.A.Bot. 1983. Greenhouse climate: from physical processes to a dynamic model [D]: [Ph. D.].Netherlands : Agricultural University, Wageningen.
G. Van Straten, H. Challa, and F. Buwalda. 2000. Towards user accepted optimal control of greenhouse climate[J]. Comput. Electron. Agriculture. 26: 221-238.
H.Uchida Frausto, J. P. Pieters, J. M. Deltour. 2003. Modelling Greenhouse Temperature by means of auto regressive models[J]. Biosystems Engineering. 84(2): 147-157.
http://e-nw.shac.gov.cn/wmfw/hwzc/hwkj/200804/t20080414_336903.htm.
http://www.hfqx..com.cn/.
http://www.jlcbs.gov.cn/html/11/26/26-4078.html
He K X , Lemmon M D. 1998. Lyapunov stability of continuous- valued systems under the supervision of discrete- event transition Systems [A]. In Henzinger T A, Sastry S ( Eds.).Hybrid Systems: Computation and Control (HSCC'98)[M]. Berlin,Germany:Springer-Verlag : 175-189.
I.Seginer, T. Boulard, B.J. Bailey. 1994. Neural network models of the greenhouse climate[J].Agric. Eng. Res. 59: 203-216.
I.Seginer. 1997. Some artificial neural network applications to greenhouse environmental control[J]. Comput. Electron. Agriculture. 18: 167-186.
I. Laribi, H. Homri, R.Mlhiri. 2006. Modeling of a greenhouse temperature : comparison between multimodel and neural approaches[J]. 7:99-404
J. Boaventura Cunha, C. Couto, and A.E. Ruano. 1997. Real-time parameter estimation of dynamic temperature models for greenhouse environmental control[J]. Control Eng. Practice .5(10): 1473-1481.
J. Boaventura Cunha. 2003. Greenhouse climate models: an overview[C]. EFITA 2003 Conference.Debrecen, Hungary. July 5-9: 823-829.
J.C.Bakker, GRA.Bot, H.Challa, N.J.Van de Braak. 1995. Geeenhouse Climate Control[M].Wageningen: Wageningen Pers.
Jitendra K. Tugnait. 1994. Linear model validation and order selection using higher-order statistics[J]. IEEE Transactions on Signal Processing. 42:1728-1736.
J. Litago, F.J. Baptista, J.F. Meneses et al. 2005. Statistical modelling of the microclimate in a naturally ventilated greenhouse. Biosystems Engineering. 92 (3): 365 - 381.
J.L Villa, A.Gauthier and N. Rakoto-Ravalontsalama. 2005. Model predictive control of mld models with intergrators[J]. Proceedings of the 2005 IEEE conference on control applications.Toronto, Canada, 641-644.
J.M. Herrero, X. Blasco, M. Martinez. et al. 2007. Non-linear robust identification of a greenhouse model using multi-objective evolutionary algorithms[J]. Biosystems engineering.98:335-346.
Johansson M and Rantzer A. 1998. Computation of piecewise quadratic Lyapunov function for hybrid systems [J]. IEEE Transaction on Automatic Control. 34(4) : 555-559.
J.P. Coelho, P.B. de Moura Oliveira, J. Boaventura Cunha. 2005. Greenhouse air temperature predictive control using the particle swarm optimisation algorithm[J]. Computers and Electronics in Agriculture. 49:330-344.
Kupers.G., van Gaalen. J., Gieling. T.h.H. and van Os, E.A. 1992. Diural Changes in the Ion Concentration of the Supply and Return Water of A Tomato Crop Grown on Rockwool[J].Acta Hort.304:291-300.
Ljung L. 2002. System Identification for the user[M]. Peking: Tsinghua University Press & Prentice Hall PTR.
Marziyeh Keshavarz, Mojtaba Barkhordary and Mohammad Reza Jahed Motlagh. 2007. Mixed logic dynamical modeling and control of steam boiler[J]. International conference on control,Automation and systems. COEX, Seoul, Korea. 299-304.
Mayne D Q, Rawlings J B, Rao C V, Scokaert POM. 2000. Constrained model predictive control:stability and optimality[J]. Automatica. 36(4): 789-814.
M Bailie, A Bailie, D Delmon. 1994. Microclimate and transpiration of greenhouse rose crops[J].Agricultural and Forest Meteorology. 71: 83-97.
M. Monsi, T. Saeki.Uber den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung fur die Stoffproduktion[J]. The Journal of Japanese Botany. 14: 22-52.
Morimoto. T., Nishina. H., Hashimoto. Y and Watake. H. 1992. Sensor for Ion-Control:An Approach to Control of Nutrient Solution in Hydroponics. Acta Hort. 304:301-308.
Morari M, Lee J H, 1999. Model predictive control: Past, present and future [J].Computers and Chemical Engineering. 23: 667-682.
M.S. Branicky, V.S. Borkar, and S.K. Mitter. 1998. A united framework for hybrid control: model and optimal control theory. IEEE Transactions on Automatic Control. 43(1):31-45.
M.Y.E1 Ghoumari, H-J.Tantau and J.Serrano. 2005. Nonlinear constrained MPC: real-time implementation of greenhouse air temperature control. Computers and Electronics in Agriculture. 49 : 345-356.
N. Sigrimis, N. Rerras. 1996. A linear model for greenhouse control[J]. Trans. ASAE. 39 (1):253-261.
Nielsen B, Madsen H. 1996. Identification of transfer functions for control of greenhouse air temperature. Journal of Agricultural Engineering Research. 60: 25-34.
N. Bennis, J. Duplaix, G. Enea, M. Halouac, H. Youlal, et al. 2008. Greenhouse climate modelling and robust control[J]. Computers and electronics in agriculture. 96-107
O.Kornera, G. Van Straten. 2007. Decision support for dynamic greenhouse climate control strategies[J]. Computers and electronics in agriculture. 1-13.
Peter J.Brockwell, Richard A.Davis. 2003. Time Series: Theory and Methods (3nd Edition)[M],New York: Springer-Verlag Inc.
Pettersson S. 1999. Analysis and design of hybrid systems [D] : [Ph. D.]. Goteborg, Sweden :Chalmers University of Technology.
Philip J.Smethurst. 2000. Soil solution and other soil analyses as indicators of nutrient supply: a review[J]. Forest ecology and management 138:397-411.
Rossiter J A, Gossner J R, Kouvaritakis B. 1996. Infinite horizon stable predictive control[J].IEEE Transactions on Automatic Control. 41(10): 1522-1527.
S.L. Patil, H.J. Tantau, V.M. Salokhe. 2008. Modelling of tropical greenhouse temperature by auto regressive and neural network models[J]. Biosystems Engineering. 99: 423 - 431.
S. Pinon , E.F. Camachoa, B. Kuchen, M. Pena. 2005. Constrained predictive control of a greenhouse[J]. Computers and Electronics in Agriculture. 49:317-329.
T. Boulard, A. Bailie. 1995. Modelling of air exchange in a greenhouse equipped with continuous roof vents[J]. Journal of Agricultural Engineering Research. 61:37-48.
T. Boulard, P. Feuilloley, C. Kittas.Natural. 1997. Ventilation performance of six greenhouse and tunnel types[J]. Journal of Agricultural Engineering Research. 67:249-266.
Torrisi F D, Bemporad A. 2004. HYSDEL-A tool for generating computational hybrid models[J].IEEE Trans on Control Systems Technology. 12(2):235-249.
Tromp L, Benveniste A, Basseville M. 1999. Fault detection and isolation in hybrid systems: A Petri- net approach [A]. In 14th IFAC World Congress [C], Beijing. 79 - 84.
Uchida Frausto H, Pieters J G 2004. Modelling greenhouse temperature using system identification by means of neural networks[J]. Neuro computing. 56(1): 423-428.
Van den Vlekkert, H.H., Kouwenhoven, J.P.M., van Wingerden. 1992. A.A.M. Application of ISFETS in Closed-Loop Systems for Horticulature[J]. Acta Hort. (ISHS). 304:309-320.
V. D. Blondel, J. N. Tsitsiklis. 1999. Complexity of stability and controllability of elementary hybrid systems[J]. Automatica. 35:479-489.
Weihong Luo, Hentrik Feije, de Zwart, et al.. 2005. Simulation of Greenhouse Management in the Subtropics, Part I-Model Validation and Scenario Study for the Winter Season[J]. Biosystems Engineering. 90(3): 307-318.
Weihong Luo, Cecilia Stanghellini, et al.. 2005. Simulation of Greenhouse Management in the Subtropics, Part H-Scenario Study for the Summer Season, Biosystems Engineering, 90(4):433-441.
Witsenhausen,H, 1966. A class of hybrid-state continuous-timedynamic systems[J]. IEEE Transactions on. Automatic control. 11(2): 161-167.
Wu Gang, Zhang Zhigang, Zhang Yuzhu and Sun Demin. 1994. Adaptive PID Control Software Based on Intelligent Supervisor and Its Application to an Acrylonitrile Plant[J]. Chinese Journal of Automation. 6(2):125-130.
Ye H, Michel A and Hou L . 1998. Stability Theory for hybrid dynamical systems [J]. IEEE Transctions on Automatic Control. 34 (4): 461-474.
Zhou Ji, Zhang Yingqi.2003. Influence of Surface Active Agent on the Response Time of Chloride Ion Selective Electrode[J]. Chemical Sensor. 23(3):62-65.