ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Original Paper

The identification of greenhouse temperature systems based on sparse FIR model

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.01.002
  • Received Date: 10 March 2014
  • Accepted Date: 24 May 2014
  • Rev Recd Date: 24 May 2014
  • Publish Date: 30 January 2015
  • Due to the effect of outside meteorological conditions, greenhouse covering materials, greenhouse structure and the growth and variety of greenhouse crops and their cultivation methods, a greenhouse temperature system has the characteristics of large time delay, nonlinearity, strong external noise disturbances, time variance. Parameter modeling can hardly describe model structures online. A method was thus proposed, which uses the finite impulse response(FIR) model to describe the temperature system and identify the time delay through the sparsity of FIR sequences. First, the sparsity of FIR sequences were analyzed. Then, according to the compressed sensing theory, a relatively small amount of data to recover the FIR sequences by solving the sparse optimization problems, hereby obtaining the time delay property of the system. Finally, the parameters of FIR model were identified. The time delay of the outside temperature, outside solar radiation, cooling pad, is 6 minutes, 1 minute and 1 minute, respectively. These results are consistent with the mechanism model of the greenhouse temperature system. As the control equipment is incapable of continuous control, the “on” and “off” status of the equipment was brought into the model which was built under the effect of the Wet Curtain-Fan. The fitting of the model was 9468%, 9414% when the Wet Curtain-Fan was on or off, suggesting that the model has higher credibility.
    Due to the effect of outside meteorological conditions, greenhouse covering materials, greenhouse structure and the growth and variety of greenhouse crops and their cultivation methods, a greenhouse temperature system has the characteristics of large time delay, nonlinearity, strong external noise disturbances, time variance. Parameter modeling can hardly describe model structures online. A method was thus proposed, which uses the finite impulse response(FIR) model to describe the temperature system and identify the time delay through the sparsity of FIR sequences. First, the sparsity of FIR sequences were analyzed. Then, according to the compressed sensing theory, a relatively small amount of data to recover the FIR sequences by solving the sparse optimization problems, hereby obtaining the time delay property of the system. Finally, the parameters of FIR model were identified. The time delay of the outside temperature, outside solar radiation, cooling pad, is 6 minutes, 1 minute and 1 minute, respectively. These results are consistent with the mechanism model of the greenhouse temperature system. As the control equipment is incapable of continuous control, the “on” and “off” status of the equipment was brought into the model which was built under the effect of the Wet Curtain-Fan. The fitting of the model was 9468%, 9414% when the Wet Curtain-Fan was on or off, suggesting that the model has higher credibility.
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  • [1]
    Qin Linlin, Wu Gang. The present situation and prospect of modeling and control of the greenhouse microclimate[J]. Automation Panorama, 2010, (2): 58-64.
    秦琳琳, 吴刚. 温室小气候环境建模与控制的现状及展望[J]. 自动化博览, 2010, (2): 58-64.
    [2]
    Bakker J C, Bot G P A, Challa H, et al. Greenhouse Climate Control: An Integrated Approach[M]. Wageningen: Wageningen Pers, 1995.
    [3]
    Boaventura Cunha J. Greenhouse climate models: an overview[C]// EFITA Conference. Debrecen, Hungary: IEEE Press, 2003: 823-829.
    [4]
    Patil S L, Tantau H J, Salokhe V M. Modeling of tropical greenhouse temperature by auto regressive and neural network models[J]. Biosystems Engineering, 2008, 99(3): 423-431.
    [5]
    Li Jin, Qin Linlin, Yue Dazhi, et al. Experiment Greenhouse Temperature System Modeling and Simulation[J]. Journal of System Simulation, 2008, 20(7): 1 869-1 875.
    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): 1 869-1 875.
    [6]
    Wen Zaiwen, Yin Wotao, Liu Xin, et al. Introduction to compressive sensing and sparse optimization[J]. Operations Research Transactions, 2012, 16(3): 49-64.
    文再文, 印卧涛, 刘歆, 等. 压缩感知和稀疏优化简介[J]. 运筹学学报, 2012, 16(3): 49-64.
    [7]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1 289-1 306.
    [8]
    Candès E J. Compressive sampling[C]// Proceedings of the International Congress of Mathematicians. Madrid, Spain: IEEE Press, 2006: 1 433-1 452.
    [9]
    Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society, Series B, 1996, 58(1): 267-288.
    [10]
    Kim S J, Koh K, Lustig M, et al. An interior-point method for large-scale l1-regularized least squares[J]. IEEE Journal on Selected Topics in Signal Processing, 2007, 1(4): 606-617.
    [11]
    Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
    [12]
    Yang J F, Zhang Y. Alternating direction algorithms for L1-problems in compressive sensing[J]. SIAM Journal on Scientific Computing, 2011, 33(1): 250-278.
    [13]
    Asif M S, Romberg J. Dynamic updating for L1 minimization[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 421-434.
    [14]
    Burger M, Moller M, Benning M, et al. An adaptive inverse scale space method for compressed sensing[R]. UCLA-CAM Report 11-08.
    [15]
    Zhang Xiaotao, Ni weidou, Li Zheng, et al. Idetifiablity of building thermal system models using on-line data[J]. Journal of Tsinghua University, 2004, 44(11): 1 544-1 547.
    张小桃, 倪维斗, 李政, 等. 基于现场数据热工对象建模的可辨识性[J]. 清华大学学报, 2004, 44(11): 1 544-1 547.
    [16]
    李嗣福.计算机控制基础[M].合肥: 中国科学技术大学出版社, 2001.
    [17]
    Ljung L. System Identification: Theory for the User[M]. 2ed, London: Prentice Hall, 1999.
    [18]
    Shi W, Ling Q, Wu G. Sparsity-enhanced linear time- invariant MIMO system identification[C]// The Chinese Control and Decision Conference. Xuzhou, China: IEEE Press, 2011: 2 026-2 029.
  • 加载中

Catalog

    [1]
    Qin Linlin, Wu Gang. The present situation and prospect of modeling and control of the greenhouse microclimate[J]. Automation Panorama, 2010, (2): 58-64.
    秦琳琳, 吴刚. 温室小气候环境建模与控制的现状及展望[J]. 自动化博览, 2010, (2): 58-64.
    [2]
    Bakker J C, Bot G P A, Challa H, et al. Greenhouse Climate Control: An Integrated Approach[M]. Wageningen: Wageningen Pers, 1995.
    [3]
    Boaventura Cunha J. Greenhouse climate models: an overview[C]// EFITA Conference. Debrecen, Hungary: IEEE Press, 2003: 823-829.
    [4]
    Patil S L, Tantau H J, Salokhe V M. Modeling of tropical greenhouse temperature by auto regressive and neural network models[J]. Biosystems Engineering, 2008, 99(3): 423-431.
    [5]
    Li Jin, Qin Linlin, Yue Dazhi, et al. Experiment Greenhouse Temperature System Modeling and Simulation[J]. Journal of System Simulation, 2008, 20(7): 1 869-1 875.
    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): 1 869-1 875.
    [6]
    Wen Zaiwen, Yin Wotao, Liu Xin, et al. Introduction to compressive sensing and sparse optimization[J]. Operations Research Transactions, 2012, 16(3): 49-64.
    文再文, 印卧涛, 刘歆, 等. 压缩感知和稀疏优化简介[J]. 运筹学学报, 2012, 16(3): 49-64.
    [7]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1 289-1 306.
    [8]
    Candès E J. Compressive sampling[C]// Proceedings of the International Congress of Mathematicians. Madrid, Spain: IEEE Press, 2006: 1 433-1 452.
    [9]
    Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society, Series B, 1996, 58(1): 267-288.
    [10]
    Kim S J, Koh K, Lustig M, et al. An interior-point method for large-scale l1-regularized least squares[J]. IEEE Journal on Selected Topics in Signal Processing, 2007, 1(4): 606-617.
    [11]
    Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
    [12]
    Yang J F, Zhang Y. Alternating direction algorithms for L1-problems in compressive sensing[J]. SIAM Journal on Scientific Computing, 2011, 33(1): 250-278.
    [13]
    Asif M S, Romberg J. Dynamic updating for L1 minimization[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 421-434.
    [14]
    Burger M, Moller M, Benning M, et al. An adaptive inverse scale space method for compressed sensing[R]. UCLA-CAM Report 11-08.
    [15]
    Zhang Xiaotao, Ni weidou, Li Zheng, et al. Idetifiablity of building thermal system models using on-line data[J]. Journal of Tsinghua University, 2004, 44(11): 1 544-1 547.
    张小桃, 倪维斗, 李政, 等. 基于现场数据热工对象建模的可辨识性[J]. 清华大学学报, 2004, 44(11): 1 544-1 547.
    [16]
    李嗣福.计算机控制基础[M].合肥: 中国科学技术大学出版社, 2001.
    [17]
    Ljung L. System Identification: Theory for the User[M]. 2ed, London: Prentice Hall, 1999.
    [18]
    Shi W, Ling Q, Wu G. Sparsity-enhanced linear time- invariant MIMO system identification[C]// The Chinese Control and Decision Conference. Xuzhou, China: IEEE Press, 2011: 2 026-2 029.

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