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Synergism of high and low level systems for the efficient management of greenhouses

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dc.contributor.author Sigrimis, NA en
dc.contributor.author Arvanitis, KG en
dc.contributor.author Pasgianos, GD en
dc.date.accessioned 2014-06-06T06:44:21Z
dc.date.available 2014-06-06T06:44:21Z
dc.date.issued 2000 en
dc.identifier.issn 01681699 en
dc.identifier.uri http://dx.doi.org/10.1016/S0168-1699(00)00134-4 en
dc.identifier.uri http://62.217.125.90/xmlui/handle/123456789/1823
dc.subject Computer control en
dc.subject Fuzzy systems en
dc.subject Knowledge based control en
dc.subject Proportional-integral-derivative (PID) control en
dc.subject Smith predictor en
dc.subject.other Artificial intelligence en
dc.subject.other Computer control en
dc.subject.other Control system analysis en
dc.subject.other Decision support systems en
dc.subject.other Expert systems en
dc.subject.other Fuzzy sets en
dc.subject.other Interfaces (computer) en
dc.subject.other Process control en
dc.subject.other Programmable logic controllers en
dc.subject.other Three term control systems en
dc.subject.other Greenhouse management systems (GMS) en
dc.subject.other Smith predictor en
dc.subject.other Greenhouses en
dc.title Synergism of high and low level systems for the efficient management of greenhouses en
heal.type conferenceItem en
heal.identifier.primary 10.1016/S0168-1699(00)00134-4 en
heal.publicationDate 2000 en
heal.abstract The advantages of using artificial intelligence (AI) decision support tools in synergism with low level process controllers or schedulers are investigated in this paper. The development of a modern control and management system for greenhouses used recent advances in software design, and development tools, to provide an open system for rapid program development. To effectively integrate expert system applications in a control and management system, an environment was built that supports all required interfaces between AI applications and the greenhouse management system (GMS). This environment incorporates a native fuzzy knowledge based system (KBS) and a number of procedural control functions, in the GMS, that can effectively interact. The programmable logic controller (PLC) houses all well-known control function blocks, in library form, callable to implement various control loop designs. Functions that have not been foreseen in the PLC control library can be instantly implemented using the open KBS system. The innovative addition of integral initial conditions on a proportional-integral-derivative (PID) controller, for repetitive load switching applications, is an example, demonstrated in this paper. The usefulness of other control blocks such as a self-adjusting Smith predictor is also tested for a real application of a mixing process with long dead time. Synergism of fuzzy decisions and fuzzy, controllers, at the supervisory level, with low level process regulators provide adaptive systems, which can optimize both long-term objectives and the short time dynamic responses. (C) 2000 Elsevier Science B.V.The advantages of using artificial intelligence (AI) decision support tools in synergism with low level process controllers or schedulers are investigated in this paper. The development of a modern control and management system for greenhouses used recent advances in software design, and development tools, to provide an open system for rapid program development. To effectively integrate expert system applications in a control and management system, an environment was built that supports all required interfaces between AI applications and the greenhouse management system (GMS). This environment incorporates a native fuzzy knowledge based system (KBS) and a number of procedural control functions, in the GMS, that can effectively interact. The programmable logic controller (PLC) houses all well-known control function blocks, in library form, callable to implement various control loop designs. Functions that have not been foreseen in the PLC control library can be instantly implemented using the open KBS system. The innovative addition of integral initial conditions on a proportional-integral-derivative (PID) controller, for repetitive load switching applications, is an example, demonstrated in this paper. The usefulness of other control blocks such as a self-adjusting Smith predictor is also tested for a real application of a mixing process with long dead time. Synergism of fuzzy decisions and fuzzy controllers, at the supervisory level, with low level process regulators provide adaptive systems, which can optimize both long-term objectives and the short time dynamic responses. en
heal.publisher Elsevier Science Publishers B.V., Amsterdam, Netherlands en
heal.journalName Computers and Electronics in Agriculture en
dc.identifier.issue 1-2 en
dc.identifier.volume 29 en
dc.identifier.doi 10.1016/S0168-1699(00)00134-4 en
dc.identifier.spage 21 en
dc.identifier.epage 39 en


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