外文翻译--农业大棚温室智能化自动控制

外文翻译--农业大棚温室智能化自动控制内容摘要：

urnal 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 bination 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 n1dimensional 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 n2dimensional 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 mdimensional vector of input variables natural vents and heating system in this work D D t is an odimensional vector of disturbances outside temperature and humidity wind speed and direction outside radiation and rain V V t is a qdimensional vector of system variables related to transpiration condensation and other processes C is an rdimensional vector of system constants t is the time Xcli and Xgri 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 nonlinear 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 plex 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 21 imization of profits Profits are calculated as the difference between the ine 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 t。