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基于 MOCC 策略的温室环境控制兼容控制算法。

A compatible control algorithm for greenhouse environment control based on MOCC strategy.

机构信息

Department of Control Science and Engineering, Tongji University, Shanghai 200092, China.

出版信息

Sensors (Basel). 2011;11(3):3281-302. doi: 10.3390/s110303281. Epub 2011 Mar 18.

DOI:10.3390/s110303281
PMID:22163799
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3231604/
Abstract

Conventional methods used for solving greenhouse environment multi-objective conflict control problems lay excessive emphasis on control performance and have inadequate consideration for both energy consumption and special requirements for plant growth. The resulting solution will cause higher energy cost. However, during the long period of work and practice, we find that it may be more reasonable to adopt interval or region control objectives instead of point control objectives. In this paper, we propose a modified compatible control algorithm, and employ Multi-Objective Compatible Control (MOCC) strategy and an extant greenhouse model to achieve greenhouse climate control based on feedback control architecture. A series of simulation experiments through various comparative studies are presented to validate the feasibility of the proposed algorithm. The results are encouraging and suggest the energy-saving application to real-world engineering problems in greenhouse production. It may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost for real-world engineering application in greenhouse production. Moreover, the proposed approach has also potential to be useful for other practical control optimization problems with the features like the greenhouse environment control system.

摘要

传统方法用于解决温室环境多目标冲突控制问题时,过于强调控制性能,而对能源消耗和植物生长的特殊要求考虑不足。由此产生的解决方案将导致更高的能源成本。然而,在长期的工作和实践中,我们发现采用区间或区域控制目标可能比采用点控制目标更合理。本文提出了一种改进的兼容控制算法,并采用多目标兼容控制(MOCC)策略和现有的温室模型,基于反馈控制架构实现温室气候控制。通过各种比较研究进行了一系列仿真实验,验证了所提出算法的可行性。结果令人鼓舞,表明该算法可应用于节能型温室生产实际工程问题。对于制定环境控制策略,实现温室生产实际工程应用中的高精度控制和低能耗,具有重要的参考价值和指导意义。此外,该方法对于具有温室环境控制系统特点的其他实际控制优化问题也具有潜在的应用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/16f34c3ce911/sensors-11-03281f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/c2f430b11d55/sensors-11-03281f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/e160aa63da79/sensors-11-03281f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/bd58f6c76701/sensors-11-03281f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/0d5196c7e4f3/sensors-11-03281f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/eacbd7e1956b/sensors-11-03281f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/80edd75113ee/sensors-11-03281f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/709f143fb67b/sensors-11-03281f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/8325ffda0b48/sensors-11-03281f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/505ff469e0a3/sensors-11-03281f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/db0690a586d5/sensors-11-03281f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/c48ef40dbb80/sensors-11-03281f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/0af430c6b2f8/sensors-11-03281f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/893feca88468/sensors-11-03281f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/16f34c3ce911/sensors-11-03281f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/c2f430b11d55/sensors-11-03281f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/e160aa63da79/sensors-11-03281f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/bd58f6c76701/sensors-11-03281f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/0d5196c7e4f3/sensors-11-03281f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/eacbd7e1956b/sensors-11-03281f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/80edd75113ee/sensors-11-03281f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/709f143fb67b/sensors-11-03281f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/8325ffda0b48/sensors-11-03281f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/505ff469e0a3/sensors-11-03281f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/db0690a586d5/sensors-11-03281f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/c48ef40dbb80/sensors-11-03281f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/0af430c6b2f8/sensors-11-03281f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/893feca88468/sensors-11-03281f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9ab/3231604/16f34c3ce911/sensors-11-03281f14.jpg

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本文引用的文献

1
Approximating the nondominated front using the Pareto Archived Evolution Strategy.使用帕累托存档进化策略逼近非支配前沿。
Evol Comput. 2000 Summer;8(2):149-72. doi: 10.1162/106365600568167.
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