ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Original Paper

Hybrid controller design and analysis for experimental greenhouse temperature system

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.04.002
  • Received Date: 23 July 2014
  • Accepted Date: 03 December 2014
  • Rev Recd Date: 03 December 2014
  • Publish Date: 30 April 2015
  • Due to the interaction between discrete on-off controls and continuous environmental factors, greenhouse temperature control systems can be regarded as a class of hybrid system. Most previous greenhouse control algorithms rely on an exquisite system model and classical or modern control theories, and fail to consider the actual conditions of greenhouses in China in their design of a controller and their system anaysis does not include the hybrid properties of the greenhouse. Based directly on the hybrid automata theory, a hybrid controller was designed for controlling the temperatures of experimental greenhouse in summer. The controller was shown to be non-blocking and deterministic in hybrid automata theory framework. Experiments were performed in the spring and summer of 2014, and the controller behaved reasonably and timely when events triggered state transitions. Further more, a controller framework containing two typical hybrid controller modes was established for winter and summer to meet the needs of continuous control all year round. And the referential time points for mode transitions were obtained by analyzing daily lowest temperaturs from 2013 to 2014.
    Due to the interaction between discrete on-off controls and continuous environmental factors, greenhouse temperature control systems can be regarded as a class of hybrid system. Most previous greenhouse control algorithms rely on an exquisite system model and classical or modern control theories, and fail to consider the actual conditions of greenhouses in China in their design of a controller and their system anaysis does not include the hybrid properties of the greenhouse. Based directly on the hybrid automata theory, a hybrid controller was designed for controlling the temperatures of experimental greenhouse in summer. The controller was shown to be non-blocking and deterministic in hybrid automata theory framework. Experiments were performed in the spring and summer of 2014, and the controller behaved reasonably and timely when events triggered state transitions. Further more, a controller framework containing two typical hybrid controller modes was established for winter and summer to meet the needs of continuous control all year round. And the referential time points for mode transitions were obtained by analyzing daily lowest temperaturs from 2013 to 2014.
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  • [1]
    Chen L J, Zhang H R, Du S F. Greenhouse temperature control system based on fuzzy theory[C]// Proceedings of the 2013 3rd International Conference on Instrumentation, Measurement, Computer, Communication and Control. Shenyang, China: IEEE Press, 2013:1673-1677.
    [2]
    Duro B, Joyce A, Mendes J F. Optimization of a seasonal storage solar system using genetic algorithms [J].Solar Energy, 2014, 101: 160-166.
    [3]
    Xu Z T, Yao Z Y, Chen L J, et al. Greenhouse air temperature predictive control using the dynamic matrix control[C]// 2013 4rth International Conference on Intelligent Control and Information Processing. Beijing, China: IEEE Press, 2013: 349-353.
    [4]
    Fathi Fourati. Multiple neural control of a greenhouse [J]. Neurocomputing, 2014, 139:138-144.
    [5]
    Wang Ziyang, Qin Linlin, Wu Gang, et al. Modeling of greenhouse temperature-humid system and model predictive control based on switching system control [J]. Transactions of the CSAE, 2008, 24(7):188-192.
    王子洋,秦琳琳,吴刚,等.基于切换控制的温室温湿度控制系统建模与预测控制[J].农业工程学报, 2008, 24(7):188-192.
    [6]
    Alur R, Courcoubetis C, Halbwachs N, et al. The algorithmic analysis of hybrid systems[J]. Theoretical Computer Science, 1995, 138(1): 3-34.
    [7]
    Antsaklis P J. Special issue on hybrid control systems: Theory and applications a brief introduction to the theory and applications of hybrid systems[J]. Proceedings of the IEEE, 2000, 88(7):879-887.
    [8]
    Campagna D, Piazza C. Hybrid automata in Systems biology: How far can we go?[J]. Theoretical Computer Science, 2008, 229(1):93-108
    [9]
    Rajaoarisoa L H, M’Sirdi N K, Balmat J. Micro-climate optimal control for an experimental greenhouse automation[C]// 2012 2nd International Conference on Communications, Computing and Control Applications. Marseilles, France: IEEE Press, 2012: 1-6.
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    Yang B, Qin L L, Wu G. Modeling and control for greenhouse temperature system based on hybrid automata[C]// 30th Chinese Control Conference, Yanta,China: IEEE Press, 2011: 1627-1631.
    阳斌,秦琳琳,吴刚.基于混杂自动机的温室温度系统建模与控制[C]//中国自动化学会控制理论专业委员D卷. 烟台, 2011:1627-1631.
    [11]
    Lygeros J. Hierarchical hybrid control of large scale systems [D].Department of Electrical Engineering, University of California, Berkeley, 1996.
    [12]
    Lygeros J, Johansson K H, Simic′ S N, et al. Dynamical properties of hybrid automata[J]. IEEE Transactions on automatic control, 2003, 48(1): 2-17.
    [13]
    Lou J H. Effect of temperature, light and media on growth of phalaenopsis[J]. Acta Agriculturae Zhejiangensis, 1995, 7 (6): 464-467.
    楼建华. 温度、光照及栽培基质对蝴蝶兰生长发育的影响[J]. 浙江农业学报, 1995, 7(6): 464- 467.
    [14]
    Abate A, Prandini M, Lygeros L, et al. Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems[J]. Automatica, 2008, 44(11): 2724-2734.
    [15]
    Ma L L, Ji J W, He C X. The study of greenhouse temperature modeling based on fuzzyneural network[C]// 2010 International Conferenceon E-Product E-Service and E-Entertainment. Henan, China: IEEE Press, 2010: 1- 4.
    [16]
    Li J, Qin L L, Yue D Z, et al. Experiment greenhouse temperature system modeling and simulation[J]. Journal of System Simulation, 2008, 20(7): 1869-1875.
    李晋,秦琳琳,岳大志,等.试验温室温度系统建模与仿真[J].系统仿真学报, 2008, 20(7): 1869-1875.
    [17]
    Qu Y, Ning D, Lai Z C, et al. Neural networks based on PID control for greenhouse temperature[J]. Transactions of the CSAE, 2011, 27(2), 307-311.
    屈毅,宁铎,赖展翅,等.温室温度控制系统的神经网络PID控制[J]. 农业工程学报, 2011, 27(2): 307-311.)
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    [1]
    Chen L J, Zhang H R, Du S F. Greenhouse temperature control system based on fuzzy theory[C]// Proceedings of the 2013 3rd International Conference on Instrumentation, Measurement, Computer, Communication and Control. Shenyang, China: IEEE Press, 2013:1673-1677.
    [2]
    Duro B, Joyce A, Mendes J F. Optimization of a seasonal storage solar system using genetic algorithms [J].Solar Energy, 2014, 101: 160-166.
    [3]
    Xu Z T, Yao Z Y, Chen L J, et al. Greenhouse air temperature predictive control using the dynamic matrix control[C]// 2013 4rth International Conference on Intelligent Control and Information Processing. Beijing, China: IEEE Press, 2013: 349-353.
    [4]
    Fathi Fourati. Multiple neural control of a greenhouse [J]. Neurocomputing, 2014, 139:138-144.
    [5]
    Wang Ziyang, Qin Linlin, Wu Gang, et al. Modeling of greenhouse temperature-humid system and model predictive control based on switching system control [J]. Transactions of the CSAE, 2008, 24(7):188-192.
    王子洋,秦琳琳,吴刚,等.基于切换控制的温室温湿度控制系统建模与预测控制[J].农业工程学报, 2008, 24(7):188-192.
    [6]
    Alur R, Courcoubetis C, Halbwachs N, et al. The algorithmic analysis of hybrid systems[J]. Theoretical Computer Science, 1995, 138(1): 3-34.
    [7]
    Antsaklis P J. Special issue on hybrid control systems: Theory and applications a brief introduction to the theory and applications of hybrid systems[J]. Proceedings of the IEEE, 2000, 88(7):879-887.
    [8]
    Campagna D, Piazza C. Hybrid automata in Systems biology: How far can we go?[J]. Theoretical Computer Science, 2008, 229(1):93-108
    [9]
    Rajaoarisoa L H, M’Sirdi N K, Balmat J. Micro-climate optimal control for an experimental greenhouse automation[C]// 2012 2nd International Conference on Communications, Computing and Control Applications. Marseilles, France: IEEE Press, 2012: 1-6.
    [10]
    Yang B, Qin L L, Wu G. Modeling and control for greenhouse temperature system based on hybrid automata[C]// 30th Chinese Control Conference, Yanta,China: IEEE Press, 2011: 1627-1631.
    阳斌,秦琳琳,吴刚.基于混杂自动机的温室温度系统建模与控制[C]//中国自动化学会控制理论专业委员D卷. 烟台, 2011:1627-1631.
    [11]
    Lygeros J. Hierarchical hybrid control of large scale systems [D].Department of Electrical Engineering, University of California, Berkeley, 1996.
    [12]
    Lygeros J, Johansson K H, Simic′ S N, et al. Dynamical properties of hybrid automata[J]. IEEE Transactions on automatic control, 2003, 48(1): 2-17.
    [13]
    Lou J H. Effect of temperature, light and media on growth of phalaenopsis[J]. Acta Agriculturae Zhejiangensis, 1995, 7 (6): 464-467.
    楼建华. 温度、光照及栽培基质对蝴蝶兰生长发育的影响[J]. 浙江农业学报, 1995, 7(6): 464- 467.
    [14]
    Abate A, Prandini M, Lygeros L, et al. Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems[J]. Automatica, 2008, 44(11): 2724-2734.
    [15]
    Ma L L, Ji J W, He C X. The study of greenhouse temperature modeling based on fuzzyneural network[C]// 2010 International Conferenceon E-Product E-Service and E-Entertainment. Henan, China: IEEE Press, 2010: 1- 4.
    [16]
    Li J, Qin L L, Yue D Z, et al. Experiment greenhouse temperature system modeling and simulation[J]. Journal of System Simulation, 2008, 20(7): 1869-1875.
    李晋,秦琳琳,岳大志,等.试验温室温度系统建模与仿真[J].系统仿真学报, 2008, 20(7): 1869-1875.
    [17]
    Qu Y, Ning D, Lai Z C, et al. Neural networks based on PID control for greenhouse temperature[J]. Transactions of the CSAE, 2011, 27(2), 307-311.
    屈毅,宁铎,赖展翅,等.温室温度控制系统的神经网络PID控制[J]. 农业工程学报, 2011, 27(2): 307-311.)

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