农业大棚温室智能化自动控制

农业大棚温室智能化自动控制 附录3 外文文献翻译——译文 农业大棚温室智能化自动控制 摘要:确定控制温室作物生长历来使用约束优化或应用人工智能技术，解决了轨迹的问题。已被用作经济利润的最优化研究的主要标准，以获得足够的作物生长的气候控制设定值。本文针对温室作物生长的问题，通过分层控制体系结构由一个高层次的多目标优化方法，在解决这个问题的 办法 鲁班奖评选办法下载鲁班奖评选办法下载鲁班奖评选办法下载企业年金办法下载企业年金办法下载 是找到白天和夜间温度参考轨迹(气候相关的设定值)和电导率(fertirrigation相关设定值)。的目标是利润最大化，果实品质，水分利用效率，这些目前正在培育的国际规则。结果说明，选择从那些获得工业的温室，在过去的八年中示出和描述。 关键词:农业;分层系统;过程控制;优化方法;产量优化 1介绍 现代农业是时下在质量和环境影响方面的规定，因此它是一个自动控制技术的应用领域已经增加了很多，在过去的几年里，温室产生的空气系统的是一个复杂的物理，化学和生物学过程，同时，使具有不同的响应时间和模式的环境因素，其特征在于由许多相互作用，它必须加以控制，以以获得最佳效果的种植者。作物生长过程是最重要的，主要受周围环境的气候变量(光合有效辐射 - PAR，温度，湿度，二氧化碳浓度，里面的空气)，水和化肥，灌溉，病虫害提供量，和文化的劳动力，如修剪和农药的治疗等等。温室是适合作物生长，因为它构成了一个封闭的环境中，可以控制气候和肥料灌溉变量。气候和肥料灌溉是两个独立的系统，不同的控制问题和目标。根据经验，不同作物品种的水和养分的要求是已知的，在实际上，第一个自动化系统控制这些变量。另一方面，市场价格的波动和环境的规则，以提高水的利用效率或其他方面加以考虑，减少肥料残留在土壤中的(如硝酸盐含量)。因此，优化生产过程，可概括为一个温室大气系统的问题，达到以下目标:的最佳作物生长(一个更大的生产与质量更好)联营公司的成本(主要是燃料，电力和化肥，减少) ，减少残留物(主要是杀虫剂和离子在土壤中)，和水的利用效率的提高。许多方法已被应用到这个问题，例如，处理的温室气候管理中的最优控制字段。 2 M0优化作物生产 一个MO优化问题可以定义为寻找决策变量的向量，它满足约束条件和优化的 目标函数一个向量，其元素。特点是竞争的措施，表现或目标的问题被视为MO优化问题，其中n目标姬(p)在变量的向量P?P的同时最小化或最大化。 问题往往没有最佳的解决 方案 气瓶 现场处置方案 .pdf气瓶 现场处置方案 .doc见习基地管理方案.doc关于群访事件的化解方案建筑工地扬尘治理专项方案下载 ，同时优化所有目标，但它有一组作为一个Pareto最优集。其中一个折衷的解决方案可以选自已知的不理想的或不占主导地位的替代解决方案设置一个决策过程。不同的标准，如物理产量，作物品质，产品质量，生产过程中的时间不同，或生产成本和风险，可配制于温室作物管理。这些标准往往会产生有争议的的气候和肥料灌溉要求，必须要解决的或明或暗地在所谓的战术层面上，种植者有几个相互冲突的目标做出决定。该解决方案的这个MO优化过程，的p?P，是最佳的日间和夜间的当前和未来的参考轨迹的温度，XTA，导电性，XEC，作物周期的其余部分。即，沿着优化的时间间隔内的空气温度是一个向量，并沿着优化的时间间隔的电导率(EC)是一个矢量。请注意，在植物生长的PAR辐射(昼夜的条件)的影响下，进行光合作用过程。此外，温度成为影响糖的生产速度通过光合作用，从而辐射和温度具有较高的辐射水平的方式，对应于较高的温度达到平衡。所以，在昼夜条件下的温度维持在较高的水平是必要的。在夜间条件下的植物都没有激活(作物不生长)，所以它不是必要的，以维持这样高的温度。出于这个原因，通常被认为是两个温度设定点:日间和夜间。这是必要的，以反白显示，虽然在连续时间的过程优化，解决了在离散的时间间隔为一个优化地平线化，且(k)项(该层是可变的，代表剩余的时间段，直到结束的农业季节)。因此，解向量，其中k是当前离散时间瞬间获得。 需要注意的是，对于提出的优化问题，温室作物生产的模型是必需的，以估计内的的气候行为和作物的生长，该算法通过不同的步骤，并涉及不同的功能目标决策变量。温室内的微气候的动态行为是涉及能量转移(辐射和热)和质量平衡(水蒸汽通量和二氧化碳浓度)的物理过程的组合。另一方面，主要取决于作物的生长和产量，在其他情况中，如灌溉和化肥，在温室内的温度，PAR辐射，CO2浓度。因此，无论是气候条件和作物生长的相互影响，其动态行为特征，可以通过不同的时间尺度。 其中XCL = XCL(t)是一个n1的维向量的温室气候状态变量的(主要的内部空气的温度和湿度，二氧化碳浓度，PAR辐射，土壤表面温度，盖温度，和植物温度)，XGR = XGR(叔)是作物生长状态变量(主要是数量的主茎上，叶面积指数(LAI)或表面土壤面积的叶片，总干物质代表所有植物成分的根，茎节点N2-维向量，叶，花和果实，不包括水，水果干物质生物量的水果，不包括水，和成熟的果实干物质或成熟果实生物量的积累)，U = U(t)是m维向量输入变量(天然通风孔和加热系统，在这项工作中)，D = D(t)是干扰(外界温度，湿度，风速和风向，室外辐射，雨)邻维向量，V = V(t)的一类q维向量，系统变量的(蒸腾，缩合，和其他进程有关)，系统常数，C是r维向量，t是时间， XCL，i和XGR，在初始时刻ti，i是已知的状态整箱整箱(t)是一个非线性函数的基础上的传质和传热的结余的fgr =的fgr(t)是一个非线性函数的基础上的植物的基本的生理过程。 地中海地区，已开发了线性和非线性模型的物理定律。这些模型可以发现深解释拉米雷斯?阿里亚斯，罗德里格斯，Berenguel和费尔南德斯(水模)，拉米雷斯-阿里亚斯等。 (增长模型)，罗德里格斯等人。 (气候模式)，罗德里格斯和Berenguel。这些模型过于复杂，这里详述，但主要的增长模型方程问题的目标和最终的MO优化问题的解释在下面的章节将描述。这些方程将用来展示如何在不同的目标(成本函数)表示为决策变量的函数的优化问题(目前和未来的温度和EC的设定值)。 2.1利润最大化 利润的计算作为新鲜水果的销售收入，并关联到他们的生产成本之间的差异VPR(t)是产量估计从市场的销售价格，XFFP(t)是获得作物生长模型的VCO(T)的新鲜水果生产，所产生的费用由供热，电力，化肥，水，t是时间，ti是作物周期的初始时间，th是最新的收获时间，同时选择由种植者。请注意，在实践中，有多个番茄作物收获在生长季节。出于这个原因，日式代表了最新的收获时间。 另一种方法是考虑在未来的收获时间(TN)，成本函数，并再次重新启动优化过程，一旦前收割工作已经产生。这两种替代品的有效期为多收获。收入取决于番茄果实的价格(千克-1，?公斤-1)，收获日期，并在每表面单位鲜重的产量(公斤米2)。价格政策需要市场模型或历史数据，这是一个非常困难的预测问题。下面的小节描述如何新鲜水果生产，XFFP(T)，以及工艺成本，压控振荡器(T)，可以预计相关的决策变量。 2.2质量最大化 利润最大化，虽然可以被理解为主要目标从种植者的角度来看，这不能总是被用来作为唯一的一个。种植者通常属于合作社或农业社会，有利于引入园艺产品进入市场。这些协会修复的政策，优质的产品，根据不同的市场需求，因此，种植者必须适应其生产这些政策的过程中，为了达到一些最低限度的质量水平。食品质量拥抱感觉属性很容易察觉到人的感官和隐藏属性，如健康和营养。在水果和蔬菜的感官性能由糖类，有机酸，挥发性化合物的量，以及颜色，形状和纹理。然而，糖和酸那些反映整体一个水果口味喜好。对于番茄作物，可溶性固形物已涉及到糖和可滴定酸度主要有机酸，因此它们可以作为果实品质的指标。坚定的水果是另一种重要的质量参数链中的种植者经销商消费者。然而，一些作品已经表明，园艺蔬菜，如西红柿或鲜花，感官质量的一些重要参数是在冲突与产 量。 番茄果实可溶性固形物，滴定酸度，果实硬度和大小可以使用下面的线性方法([Dorais等人，2001年XTA(t)和XEC(T)(决策变量)]和[Sonneveld和面包车博格，1991])(15)Y(T)= A + B(X(T)-G(X(T))) 其中Y(t)为变量的计算(可溶性固形物，滴定酸度，果实硬度，或大小)，X(t)是相关的决策变量(XEC为VSSol(T)(T)，腹侧被盖区(T)， vfs的(t)的和XTA(t)的VFF(t))的，在Y(t)的系数，是一个常数增量，b为增量在Y(t)的系数，在X(t)的单位的增量，并G(X(t))的代表在Y(T)，其中有一个增量的X(t)的阈值是一个分段函数。 2.3水利用效率的最大化 这个目标优化问题明确纳入环境有关的目的。在半干旱的气候，如地中海的，水是非常稀缺和昂贵的资源，主要是在一些一年四季。有些作者认为，在这样的地区，是由生产力可用的水和用水效率使用。这样，适当管理的水是必需的。与显式包含这一目标，种植者可以选择提供的期望的耗水量，在生长周期从帕累托前沿的解决方案。这一目标的尝试使用的水量足以作物生长发育的密切关系，所提供的营养液的浓度。在本文中，水分利用效率被认为是类似的生物量的效率之间的关系定义为新鲜水果的物质生产与供给的水。 2.4多目标优化问题 所有这些目标中的变量是空气温度，XTA和/或欧盟，XEC，(XFFP(T)的FSF(T)，西南(T)，腹侧被盖区(T)，VSSol(T)的功能， VFS(T)，VFF(T))，以及衡量的干扰，如PAR辐射或二氧化碳浓度。也就是说，目标函数可以表示为对于i = 1,2,3，是沿着优化的时间间隔内的空气温度的向量是一个向量沿着优化的时间间隔的EC，Θ是一个向量测的扰动具有沿水平优化预测。 MO优化问题的解决提供了欧共体内的空气温度控制地平线其余的日间和夜间的设定轨迹。恒定的日间和夜间的设定点定义，稳定状态模型的温室气候和番茄作物， 总结 初级经济法重点总结下载党员个人总结TXt高中句型全总结.doc高中句型全总结.doc理论力学知识点总结pdf 在Eqs.Although几种技术已被评估为解决MO优化问题，在这种情况下，一个目标实现算法已被用于(序贯二次规划SQP)。确定每个目标的重点，通过使用权重，按顺序在每个迭代修改。的约束被定义为从专家的知识获得的最大和最小的温度和EC值表明“最佳”番茄的生长温度和通过分析局部数据从历史系列。由此产生的约束条件改变整个每年的时间与过去的二十年收集的数据的基础上设计的图案。 3多级递阶控制结构 动态参与温室生产过程中呈现出不同的时间尺度上，如上所述，即内部温室 气候，作物快速动力学(即蒸腾作用，光合作用和呼吸作用)，和缓慢的的作物发育(即作物生长和果实的变化)。因此，多层分级控制架构已经提出并使用(Rodriguez等人，2003年和罗德里格斯等人，2008]) 3.1作物生长控制层 考虑到长期目标(市场价格，收获日期和所需的质量)和长期预测的增长状态，使用修改后的模型(拉米雷斯-阿里亚斯等人，2004)进行优化计算的温室内温度的设定值轨迹和欧盟一起考虑控制范围内(通常是65天为一个淡旺季-260决策变量 - 或120天为一个漫长的赛季-480决策变量)。灌溉模型也已开发，控制和优化的目的。 长期天气预测，这是逻辑上具有较高程度的不确定性的要素之一，是使用一个软件工具，访问由西班牙国家气象局的天气预测，未来八天向前，产生模式在几个指标(清晰度，最大，平均和最低气温，太阳辐射)，在本地搜索历史气候序列数据库生成模式，更好地适合。以这种方式，以所选择的序列作为短期天气预报，估计作物周期的其余部分被从该短序列和使用从历史数据库中的一个数据窗口生成。通过滚动的方法，在第二层进行修改，降低不确定性的相关程度高。 3.2设定适应层 在这一层中，被发送到下层为第二天的设定值被修改和更新，以避免不可行性问题，并允许达到参考值。考虑在上层，短期内的天气预报(具有较低程度的不确定性)，当前状态的作物产生的轨迹，这些修改和短期种植者目标(考虑到他/她的技能和作物状态，这是必要的自由度，让种植者的分层控制系统进行交互)。然后，该信息是用在上面描述的模型，以模拟的温室的行为，并评价，如果所提供的设定点可以达到。在优化过程被重复修改(减少或增加设定值)，根据仿真结果的约束。当设定点是可到达的，它们被发送到下层。 3.3气候控制和营养层 从上层使用的温度和EC设定点，控制器计算的适当的控制信号，致动器。所开发的控制算法包括范围宽，从馈控制，自适应控制，预测控制，混合控制。这显然是有限的引用列表和温度控制上的许多重要文件都没有提到，由于空间的限制。 4。结论 在这项工作中，一个MO优化问题已经提出，温室作物生长管理测试，获得三个目标:经济利益的最大化，果实品质，水分利用效率的折中解决方案。这个优化方案已经集成到一个层次的控制架构，使日间和夜间的温度和EC通过整个作物周期(使用滚动战略)的设定值自动生成。结果表明短期和长期两个作物周 期的逻辑轨迹。在未来8年，提供实时的结果在工业温室进行建模，仿真，控制 和优化的温室作物生产 工作总结 关于社区教育工作总结关于年中工作总结关于校园安全工作总结关于校园安全工作总结关于意识形态工作总结 研究。 附录4 外文文献翻译——原文 Agricultural greenhouses greenhouse intelligent automatic control Abstract:The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described. Keywords: Agriculture; Hierarchical systems; Process control; Optimization methods; Yield optimization 1. Introduction Modern agriculture is nowadays subject to regulations in terms of quality and environmental impact and thus it is a field where the application of automatic control techniques has increased a lot during the last few years The greenhouse production agrosystem is a complex of physical, chemical and biological processes, taking place simultaneously, reacting with different response times and patterns to environmental factors, and characterized by many interactions (Challa & van Straten, 1993), which must be controlled in order to obtain the best results for the grower. Crop growth is the most important process and is mainly influenced by surrounding environmental PAR, temperature, climatic variables (Photosynthetically Active Radiation — humidity, and CO2 concentration of the inside air), the amount of water and fertilizers supplied by irrigation, pests and diseases, and culture labors such as pruning and pesticide treatments among others. A greenhouse is ideal for crop growing since it constitutes a closed environment in which climatic and Fertilizer irrigation variables can be controlled. Climate and Fertilizer irrigation are two independent systems with different control problems and objectives. Empirically, the water and nutrient requirements of the different crop species are known and, in fact, the first automated systems were those that control these variables. On the other hand, the market price fluctuations and the environment rules to improve the water-use efficiency or reduce the fertilizer residues in the soil (such as the nitrate contents) are other aspects to be taken into account. Therefore, the optimal production process in a greenhouse agrosystem may be summarized as the problem to reaching the following objectives: an optimal crop growth (a bigger production with a better quality), reduction of the associate costs (mainly fuel, electricity, and fertilizers), reduction of residues (mainly pesticides and ions in soil), and the improvement of the water use efficiency. Many approaches have already been applied to this problem, for instance, dealing with the management of greenhouse climate in the optimal control field, e.g. Challa and van 2. MO optimization in crop production An MO optimization problem can be defined as finding a vector of decision variables which satisfies constraints and optimizes a vector whose elements represent objective functions The problems characterized by competing measures of performance or objectives are considered as MO optimization problems, where n objectives Ji(p) in the vector of variables p?P are simultaneously minimized (or maximized) 。 The problem often has no optimal solution that simultaneously optimize all objectives, but it has a set of suboptimal or non-dominated alternative solutions known as a Pareto optimal set , where a compromise solution may be selected from that set by a decision process. Different criteria, such as physical yield, crop quality, product quality, timing of the production process, or production costs and risks, can be formulated within greenhouse crop management. These criteria will often give rise to controversial climate and 肥料灌溉 requirements, which have to be solved explicitly or implicitly at the so-called tactical level, where the grower has to make decisions about several conflicting objectives. The solution of this MO optimization process, p?P, is the optimal diurnal and nocturnal present and future reference trajectories of temperature, Xta, and electrical conductivity, XEC, for the rest of the crop cycle. That is, where is a vector of the inside air temperature along the optimization intervals, and is a vector of the electrical conductivity (EC) along the optimization intervals. Notice that the plants grow under the influence of the PAR radiation (diurnal conditions), performing the photosynthesis process. Furthermore, the temperature influences the speed of sugar production by photosynthesis, and thus radiation and temperature have to be in balance in the way that a higher radiation level corresponds to a higher temperature. So, under diurnal conditions it is necessary to maintain the temperature at a high level. In nocturnal conditions, the plants are not active (the crop does not grow), so it is not necessary to maintain such a high temperature. For this reason, two temperature setpoints are usually considered: diurnal and nocturnal . It is necessary to highlight that although the process optimization is presented in continuous time, it is solved in discrete time intervals for an optimization horizon, Nf(k) (this horizon is variable and represents the remaining intervals until the end of the agricultural season). Thus, the solution vectors and are obtained as where k is the current discrete time instant. Notice that, for the proposed optimization problem, a greenhouse crop production model is required in order to estimate the inner climate behavior and the crop growth through the different steps of the algorithm and relate the different function objectives to the decision variables. The dynamic behavior of the microclimate inside the greenhouse is a combination of physical processes involving energy transfer (radiation and heat) and mass balance (water vapor fluxes and CO2 concentration). On the other hand, the crop growth and yield mainly depend, among other conditions such as irrigation and fertilizers, on the inside temperature of the greenhouse, the PAR radiation, and the CO2 concentration. Thus, both climate conditions and crop growth influence each other and their dynamic behavior can be characterized by different time scales. Hence, the crop growth in response to the environment can be described by two dynamic models, represented by two systems of differential equations with a time scale associated to their dynamics, which can be represented by where Xcl=Xcl(t) is an n1-dimensional vector of greenhouse climate state variables (mainly the inside air temperature and humidity, CO2 concentration, PAR radiation, soil surface temperature, cover temperature, and plant temperature), Xgr=Xgr(t) is an n2-dimensional vector of crop growth state variables (mainly number of nodes on the main stem, leaf area index (LAI) or surface of leaves by soil area, total dry matter which represents all the plant constituents–root, stem, leaves, flower and fruit–excluding water, fruit dry matter being the biomass of the fruits excluding water, and mature fruit dry matter or mature fruit biomass accumulation), U=U(t) is an m-dimensional vector of input variables (natural vents and heating system in this work), D=D(t) is an o-dimensional vector of disturbances (outside temperature and humidity, wind speed and direction, outside radiation, and rain), V=V(t) is a q-dimensional vector of system variables (related to transpiration, condensation, and other processes), C is an r-dimensional vector of system constants, t is the time, Xcl,i and Xgr,i are the known states at the initial time ti, fcl=fcl(t) is a nonlinear function based on mass and heat transfer balances, and fgr=fgr(t) is a non-linear function based on the basic physiological processes of the plants. For the Mediterranean area, the authors have developed linear and nonlinear models using physical laws. These models are too complex to be detailed here, but the main growth model equations will be described in the following sections where the problem objectives and the final MO optimization problem are explained. These equations will be used to show how the different objectives (cost functions) are expressed as functions of the decision variables of the optimization problem (present and future temperature and EC setpoints). 2.1. Maximization of profits Profits are calculated as the difference between the income from the selling of the fresh fruits and the costs associated to their production where Vpr(t) is the selling price of the production (estimated from the market), XFFP(t) is the fresh fruit production obtained from the crop growth model Vcos(t) are the costs incurred by heating, electricity, fertilizers, and water , t is the time, ti is the initial time of crop cycle, and th is the latest harvesting time, both selected by the grower. Notice that in practice, the tomato crop has multiple harvest during the growing season. For that reason, th represents the latest harvesting time in Eq. An alternative is to consider the next harvesting time (tn) in the cost function and restarting the optimization process again once the previous harvest has been produced. Both alternatives are valid for multiple harvest. The income depends on the price of tomato fruits ($kg?1,?kg?1), the harvesting dates, and on the yield in fresh weight per surface unit (kg m?2). The price policy requires market models or historical data, this being a very difficult prediction problem. The following subsections describe how the fresh fruit production, XFFP(t), and the process costs, Vcos(t), can be estimated and related with the decision variables, . 2.2. Maximization of quality Although maximizing the profits can be understood as the main objective from the growers’ point of view, this cannot always be used as the only one. The growers usually belong to cooperatives or agrarian societies that facilitate the introduction of the horticultural products into the market. These associations fix the policies on quality products based on the different market requirements, and thus the growers must adapt their production process to those policies in order to reach some minimum quality levels. Food quality embraces both sensory attributes that are readily perceived by the human senses and hidden attributes such as healthiness and nutrition (Shewfelt, 1999). In fruits and vegetables, the sensory properties are determined by the amount of sugars, organic acids, and volatile compounds, as well as color, shape, and texture. However, sugars and acids are those reflecting overall taste preferences for a fruit. For a tomato crop, soluble solids have been related to sugars ( [Li et al., 2001] and [Sonneveld and van der Burg, 1991]) and titratable acidity to main organic acids ( [Auerswald et al., 1999] and [Sonneveld and van der Burg, 1991]); thus they can be used as indicators of fruit quality. Firmness of the fruit is another important quality parameter in the chain grower–dealer–consumer. Nevertheless, some works have shown that in horticultural vegetables, such as tomato or flowers, some important parameters of sensory quality are in conflict with yield ( [Dorais et al., 2001], [Li et al., 2001] and [Sonneveld and van der Burg, 1991]). Hence, the fruit quality can be expressed as (14) where VSSol(t) is the soluble solids concentration in the fruit, Vta(t) is the titratable acidity in fruits, Vff(t) is the fruit firmness, Vfs(t) is fruit size, and wssol, wta, wff, and wfs are weighting parameters. In tomato fruits, soluble solids, titratable acidity, fruit firmness and size may be related to Xta(t) and XEC(t) (decision variables) using the following linear approach ( [Dorais et al., 2001] and [Sonneveld and van der Burg, 1991]) (15)Y(t)=a+b(X(t)?g(X(t))) where Y(t) is the variable to be calculated (soluble solids, titratable acidity, fruit firmness, or size), X(t) is the related decision variable (XEC(t) for VSSol(t), Vta(t), Vfs(t); and Xta(t) for Vff(t)), a is a constant increment coefficient in Y(t), b is the increment coefficient in Y(t) per unit of increment in X(t), and g(X(t)) is a piecewise function representing a threshold of X(t) over which there is an increment in Y(t). 2.3. Maximization of water-use efficiency The explicit inclusion of this objective in the optimization problem has an environment-related purpose. In semi-arid climates, such as Mediterranean ones, water is a very scarce and expensive resource, mainly during some seasons of the year. Some authors maintain that the productivity in such regions is determined by the available water and the water efficiency use (Hsiao & Xu, 2000). Thus, an adequate management of water is required. With the explicit inclusion of this objective, the grower can select a solution from the Pareto front providing the desired water consumption during the growing cycle. This objective tries to use the water quantities adequate to the crop growth in close relationship to the supplied concentration of nutrient solution. In this paper, water-use efficiency is considered like the biomass efficiency defined as the relationship between the fresh fruit matter production and the water supplied. 2.4. Multiobjective optimization problem All the variables presented in these objectives are functions of the air temperature, Xta, and/or the EC, XEC, (XFFP(t),Fsf(t),Wsw(t),Vta(t),VSSol(t),Vfs(t),Vff(t)), as well as of measurable disturbances such as PAR radiation or CO2 concentration. That is, the objective functions can be expressed as for i=1,2,3, where is a vector of the inside air temperature along the optimization interval, is a vector of the EC along the optimization interval, and Θ is a vector of the measurable disturbances that have to be predicted along the optimization horizon. The solution to the MO optimization problem provides both diurnal and nocturnal setpoint trajectories of EC and inside air temperature for the rest of the control horizon. Constant diurnal and nocturnal setpoints are defined, and steady state models of greenhouse climate and tomato crop, summarized in Eqs.Although several techniques have been evaluated to solve the MO optimization problem (Liu et al., 2003), in this case, a goal attainment algorithm has been used (sequential quadratic programing SQP-based). Priorities for each objective are determined by using weights that are sequentially modified in each iteration. The constraints are defined by maximum and minimum values of temperature and EC obtained from experts’ knowledge that indicate ―optimal‖ growing temperatures for tomato and by analyzing local data from historical series. The resulting constraints are changing throughout time with a yearly pattern designed on the basis of the last twenty years collected data. 3. Multilevel hierarchical control architecture .The dynamics involved in the greenhouse production process present different time scales as described above, namely, internal greenhouse climate, fast crop dynamics (i.e. transpiration, photosynthesis, and respiration), and slow crop development (i.e. crop growth and fruit changes). Hence, a multilayer hierarchical control architecture has been proposed and used 。 3.1. Crop growth control layer Taking into account the long-term objectives (market prices, harvesting dates, and required quality) and the long-term predictions of the growth state using the modified Tomgro model (Ramírez-Arias et al., 2004) (for the estimation of yield and profits), the optimization is performed to calculate the setpoint trajectories of the inside greenhouse temperature and the EC along the considered control horizon (typically 65 days for a short season ?260 decision variables — or 120 days for a long season ?480 decision variables). Models for irrigation have also been developed for control and optimization purposes 。 The long-term weather prediction, which is logically one of the elements with a higher degree of uncertainty and is performed using a software tool that accesses the weather predictions given by the Spanish National Institute of Meteorology for the next eight days forward, generates patterns based on several indexes (clarity, maximum, mean and minimum temperatures, and solar radiation), and searches within a local historical database for a climatic sequence that better fits the generated patterns. In this way, taking the selected sequence as a short term weather prediction, the estimation for the rest of the crop cycle is generated starting from this short sequence and using a data window from the historical database. The associated high degree of uncertainties is reduced through the receding horizon approach and modifications performed in the second layer. 3.2. Setpoint adaptation layer In this layer, the setpoints to be sent to the lower layer for the next day are modified and updated in order to avoid unfeasibility problems and allow reaching the reference values. These modifications are performed considering the trajectories generated in the upper layer, the short term weather prediction (that has a lower degree of uncertainty), the current state of the crop, and the short term grower goals (considering his/her skill and the crop status, this being a necessary degree of freedom to let the grower interact with the hierarchical control system). Then, this information is used within the models described above in order to simulate the greenhouse behavior and to evaluate if the provided setpoints can be reached. The optimization process is repeated modifying the constraints (diminishing or increasing the setpoints) according to the simulation results. When the setpoints are reachable, they are sent to the lower layer. 3.3. Climate control and nutrition layer Using the temperature and EC setpoints from the upper layers, the controllers compute the adequate control signals for the actuators. The control algorithms developed include a wide range from feedforward control, adaptive control, predictive control, and hybrid control. This list of references is evidently limited and many important papers on temperature control are not mentioned due to space constraints。 4. Conclusion In this work, an MO optimization problem has been proposed and tested for greenhouse crop growth management, obtaining tradeoff solutions of three objectives: maximization of economic benefits, fruit quality, and water-use efficiency. This optimization scheme has been integrated into a hierarchical control architecture that allows the automatic generation of setpoints for diurnal and nocturnal temperatures and EC through a whole crop cycle (using a receding horizon strategy). The obtained results show logical trajectories both in short and long crop cycles. The work summarizes research performed on modeling, simulation, control, and optimization of greenhouse crop production during eight years providing real results in an industrial greenhouse.