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Expert controller in multi-variable System of Temperature and Humidity

Year 2017, , 51 - 59, 09.10.2017

Ma Yinping , Zu-yan Lu

https://doi.org/10.24107/ijeas.331323
Cited By: 1

Abstract

In order to ensure the humidity- and heat-resistance ability of the equipment, this paper, combined with the environment simulation test of an engineering equipment system, designs the control system of humidity-heat simulation test with S7-300 as the control core. This paper analyzes the temperature and humidity model in laboratory, and applies the principle of the expert control to the system control algorithm to improve the control performance of the system, which shows the characteristics of lagging, nonlinearity, interactive coupling of temperature control and humidity control, etc.

Keywords

nonlinearity interactive coupling

References

  • [1] Shi X, Zhu N, Zheng G. The combined effect of temperature, relative humidity and work intensity on human strain in hot and humid environments[J]. Building & Environment, 2013, 69(11):72-80.
  • [2] Mukaidani H. An LMI approach to decentralized guaranteed cost control for a class of uncertain nonlinear large-scale delay systems[J]. Journal of Mathematical Analysis & Applications, 2004, 300(1):17-29.
  • [3] Ghaffari V, Naghavi S V, Safavi A A. Robust model predictive control of a class of uncertain nonlinear systems with application to typical CSTR problems[J]. Journal of Process Control, 2013, 23(4):493-499.
  • [4] Lewis G, Coulson M, Vergnet C, et al. Proposed revision of the higher tier testing requirements for EPPO standard PP1/170: test methods for evaluating the side-effects of plant protection products on honeybees.[J]. Julius-Kühn-Archiv, 2010(423):34-42.
  • [5] Caia Z X, Wangb Y N, Caia J F. A real-time expert control system[J]. Artificial Intelligence in Engineering, 1996, 10(4):317-322.
  • [6] Good J, Blandford A. Incorporating human factors concerns into the design and safety engineering of complex control systems[C]// International Conference on Human Interfaces in Control Rooms, Cockpits and Command Centres. IET, 1999:51-56.
  • [7] Kahn M G, Bailey T C, Steib S A, et al. Statistical process control methods for expert system performance monitoring.[J]. Journal of the American Medical Informatics Association Jamia, 1996, 3(4):258-269.
  • [8] Arakaki J, Miyagi P E. Degeneration Methods in Intelligent Building Control System Design[J]. Information Technology for Balanced Manufacturing Systems, 2006, 220:469-478.
  • [9] Rt M Y R G. Knowledge elicitation for fuzzy rule based decision support systems and system interface evaluation and design.[J].
  • [10] Evans M, Kennedy J. Integration of Adaptive Neuro Fuzzy Inference Systems and principal component analysis for the control of tertiary scale formation on tinplate at a hot mill[J]. Expert Systems with Applications, 2014, 41(15):6662-6675.
  • [11] Schwedler M, Bakkum B. Upgrading chilled-water systems[J]. Ashrae Journal, 2009, 51(11):16-32.
  • [12] Sarkar M. Simplified thermodynamic modeling of chilled water coils based on bypass factors[J]. Energy & Buildings, 2015, 103(3-4):384-395.

Year 2017, , 51 - 59, 09.10.2017

Ma Yinping , Zu-yan Lu

https://doi.org/10.24107/ijeas.331323
Cited By: 1

Abstract

References

  • [1] Shi X, Zhu N, Zheng G. The combined effect of temperature, relative humidity and work intensity on human strain in hot and humid environments[J]. Building & Environment, 2013, 69(11):72-80.
  • [2] Mukaidani H. An LMI approach to decentralized guaranteed cost control for a class of uncertain nonlinear large-scale delay systems[J]. Journal of Mathematical Analysis & Applications, 2004, 300(1):17-29.
  • [3] Ghaffari V, Naghavi S V, Safavi A A. Robust model predictive control of a class of uncertain nonlinear systems with application to typical CSTR problems[J]. Journal of Process Control, 2013, 23(4):493-499.
  • [4] Lewis G, Coulson M, Vergnet C, et al. Proposed revision of the higher tier testing requirements for EPPO standard PP1/170: test methods for evaluating the side-effects of plant protection products on honeybees.[J]. Julius-Kühn-Archiv, 2010(423):34-42.
  • [5] Caia Z X, Wangb Y N, Caia J F. A real-time expert control system[J]. Artificial Intelligence in Engineering, 1996, 10(4):317-322.
  • [6] Good J, Blandford A. Incorporating human factors concerns into the design and safety engineering of complex control systems[C]// International Conference on Human Interfaces in Control Rooms, Cockpits and Command Centres. IET, 1999:51-56.
  • [7] Kahn M G, Bailey T C, Steib S A, et al. Statistical process control methods for expert system performance monitoring.[J]. Journal of the American Medical Informatics Association Jamia, 1996, 3(4):258-269.
  • [8] Arakaki J, Miyagi P E. Degeneration Methods in Intelligent Building Control System Design[J]. Information Technology for Balanced Manufacturing Systems, 2006, 220:469-478.
  • [9] Rt M Y R G. Knowledge elicitation for fuzzy rule based decision support systems and system interface evaluation and design.[J].
  • [10] Evans M, Kennedy J. Integration of Adaptive Neuro Fuzzy Inference Systems and principal component analysis for the control of tertiary scale formation on tinplate at a hot mill[J]. Expert Systems with Applications, 2014, 41(15):6662-6675.
  • [11] Schwedler M, Bakkum B. Upgrading chilled-water systems[J]. Ashrae Journal, 2009, 51(11):16-32.
  • [12] Sarkar M. Simplified thermodynamic modeling of chilled water coils based on bypass factors[J]. Energy & Buildings, 2015, 103(3-4):384-395.
There are 12 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Ma Yinping

Zu-yan Lu This is me 0000-0002-8734-0053

Publication Date October 9, 2017
Acceptance Date September 28, 2017
Published in Issue Year 2017

Cite

APA Yinping, M., & Lu, Z.-y. (2017). Expert controller in multi-variable System of Temperature and Humidity. International Journal of Engineering and Applied Sciences, 9(3), 51-59. https://doi.org/10.24107/ijeas.331323
AMA Yinping M, Lu Zy. Expert controller in multi-variable System of Temperature and Humidity. IJEAS. October 2017;9(3):51-59. doi:10.24107/ijeas.331323
Chicago Yinping, Ma, and Zu-yan Lu. “Expert Controller in Multi-Variable System of Temperature and Humidity”. International Journal of Engineering and Applied Sciences 9, no. 3 (October 2017): 51-59. https://doi.org/10.24107/ijeas.331323.
EndNote Yinping M, Lu Z-y (October 1, 2017) Expert controller in multi-variable System of Temperature and Humidity. International Journal of Engineering and Applied Sciences 9 3 51–59.
IEEE M. Yinping and Z.-y. Lu, “Expert controller in multi-variable System of Temperature and Humidity”, IJEAS, vol. 9, no. 3, pp. 51–59, 2017, doi: 10.24107/ijeas.331323.
ISNAD Yinping, Ma - Lu, Zu-yan. “Expert Controller in Multi-Variable System of Temperature and Humidity”. International Journal of Engineering and Applied Sciences 9/3 (October 2017), 51-59. https://doi.org/10.24107/ijeas.331323.
JAMA Yinping M, Lu Z-y. Expert controller in multi-variable System of Temperature and Humidity. IJEAS. 2017;9:51–59.
MLA Yinping, Ma and Zu-yan Lu. “Expert Controller in Multi-Variable System of Temperature and Humidity”. International Journal of Engineering and Applied Sciences, vol. 9, no. 3, 2017, pp. 51-59, doi:10.24107/ijeas.331323.
Vancouver Yinping M, Lu Z-y. Expert controller in multi-variable System of Temperature and Humidity. IJEAS. 2017;9(3):51-9.

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https://doi.org/10.3390/mi11110999

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