A feasibility study of sliding mode predictive control for greenhouses

Detalhes bibliográficos
Autor(a) principal: Josenalde Barbosa Oliveira
Data de Publicação: 2016
Outros Autores: José Boaventura, Paulo Moura Oliveira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5015
http://dx.doi.org/10.1002/oca.2189
Resumo: In this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright (c) 2015 John Wiley & Sons, Ltd.
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spelling A feasibility study of sliding mode predictive control for greenhousesIn this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright (c) 2015 John Wiley & Sons, Ltd.2017-12-28T02:18:19Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5015http://dx.doi.org/10.1002/oca.2189engJosenalde Barbosa OliveiraJosé BoaventuraPaulo Moura Oliveirainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:20Zoai:repositorio.inesctec.pt:123456789/5015Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:58.124112Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A feasibility study of sliding mode predictive control for greenhouses
title A feasibility study of sliding mode predictive control for greenhouses
spellingShingle A feasibility study of sliding mode predictive control for greenhouses
Josenalde Barbosa Oliveira
title_short A feasibility study of sliding mode predictive control for greenhouses
title_full A feasibility study of sliding mode predictive control for greenhouses
title_fullStr A feasibility study of sliding mode predictive control for greenhouses
title_full_unstemmed A feasibility study of sliding mode predictive control for greenhouses
title_sort A feasibility study of sliding mode predictive control for greenhouses
author Josenalde Barbosa Oliveira
author_facet Josenalde Barbosa Oliveira
José Boaventura
Paulo Moura Oliveira
author_role author
author2 José Boaventura
Paulo Moura Oliveira
author2_role author
author
dc.contributor.author.fl_str_mv Josenalde Barbosa Oliveira
José Boaventura
Paulo Moura Oliveira
description In this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright (c) 2015 John Wiley & Sons, Ltd.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2017-12-28T02:18:19Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/5015
http://dx.doi.org/10.1002/oca.2189
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http://dx.doi.org/10.1002/oca.2189
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