【正文】 約束當(dāng)設(shè)定點(diǎn)是可到達(dá)的它們被發(fā)送到下層 33 氣候控制和營(yíng)養(yǎng)層 從上層使用的溫度和 EC 設(shè)定點(diǎn)控制器計(jì)算的適當(dāng)?shù)目刂菩盘?hào)致動(dòng)器所開發(fā)的控制算法包括范圍寬從饋控制自適應(yīng)控制預(yù)測(cè)控制混合控制這顯然是有限的引用列表和溫度控制上的許多重要文件都沒(méi)有提到由于空間的限制 4 結(jié)論 在這項(xiàng)工作中一個(gè) MO 優(yōu)化問(wèn)題已經(jīng)提出溫室作物生長(zhǎng)管理測(cè)試獲得三個(gè)目標(biāo)經(jīng)濟(jì)利益的最大化果實(shí)品質(zhì)水分利用效率的折中解決方案這個(gè)優(yōu)化方案已經(jīng)集成到一個(gè)層次的控制架構(gòu)使日間和夜間的溫度和 EC 通過(guò)整個(gè)作物周期使用滾動(dòng)戰(zhàn)略的設(shè)定值自動(dòng)生成結(jié)果表明短期和長(zhǎng)期兩個(gè)作物周期的邏輯軌跡在未來(lái) 8 年提供實(shí)時(shí)的結(jié)果在工業(yè)溫室進(jìn)行建模仿真控制和優(yōu)化的溫室作物生產(chǎn)工作總結(jié)研究 附錄 4 外文文獻(xiàn)翻譯原文 Agricultural greenhouses greenhouse intelligent automatic control AbstractThe 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 highlevel multiobjective optimization approach where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures climaterelated setpoints and electrical conductivity fertirrigationrelated setpoints The objectives are to imize profit fruit quality and wateruse 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 plex 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 climatic variables Photosynthetically Active Radiation PAR temperature 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 wateruse 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 eg 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 peting 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 imized The problem often has no optimal solution that simultaneously optimize all objectives but it has a set of suboptimal or nondominated alternative solutions known as a Pareto optimal set where a promise 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 socalled 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 diur