Water Level Control of Nuclear Power Plant Steam Generator Based on Intelligent Virtual Reference Feedback Tuning

  • Zhi Han
  • Hu Qi
  • Ling WangEmail author
  • Muhammad Ilyas Menhas
  • Minrui Fei
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 925)


The steam generator is one of the most important equipment in nuclear power plants. The water level control of steam generators is a challenging problem due to its complicated characteristics. This paper studies a novel intelligent virtual reference feedback tuning method based on human learning optimization (IVRFTH) and applies it to the water level control, in which the optimal controller can be directly designed without knowing the mathematical model of the controlled object. The simulation results show that the developed IVRFTH surpasses the standard IVRFT method with the introduction of human learning optimization (HLO). As IVRFTH is easy to design the optimal controller without the model information, it is very promising for the engineering application.


Intelligent VRFT Human learning optimization Steam generator Water level control Data-driven control 



This work is supported by National Natural Science Foundation of China (Grant No. 61633016 & 61703262), Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 16010500300 and 15220710400, Shanghai Sailing Program under Grant No. 16YF1403700, and Natural Science Foundation of Shanghai (No.18ZR1415100).


  1. 1.
    Li, C., Ye, J., Zhao, M.: Multi-model control for water lever of steam generator in nuclear power plants based on linear active disturbance rejection. Autom. Instrum. 32(1), 46–50 (2017)Google Scholar
  2. 2.
    Zhang, Z., Hu, L.: Performance assessment for the water level control system in steam generator of the nuclear power plant. Ann. Nucl. Energy 45, 94–105 (2012)CrossRefGoogle Scholar
  3. 3.
    Gu, J., Ji, N., Sun, Y., Wang, D.: The multimodel-based active disturbance rejection control for water level of steam generator in nuclear power plants. J. Chin. Soc. Power Eng. 32(5), 373–377 (2012)Google Scholar
  4. 4.
    Tan, W.: Water level control for a nuclear steam generator. Nucl. Eng. Des. 241(5), 1873–1880 (2011)CrossRefGoogle Scholar
  5. 5.
    Wei, L., Fang, F., Shi, Y.: Adaptive backstepping-based composite nonlinear feedback water level control for the nuclear U-tube steam generator. IEEE Trans. Control Syst. Technol. 22(1), 369–377 (2014)CrossRefGoogle Scholar
  6. 6.
    Thakur, A., Singh, H., Wadhwani, S.: Designing of fuzzy logic controller for liquid level controlling. Int. J. u-and e-Serv. Sci. Technol. 8(6), 267–276 (2015)CrossRefGoogle Scholar
  7. 7.
    Habibiyan, H., Setayeshi, S., Arab-Alibeik, H.: A fuzzy-gain-scheduled neural controller for nuclear steam generators. J. Ann. Nucl. Energ. 31(15), 1765–1781 (2004)CrossRefGoogle Scholar
  8. 8.
    Wang, L., Ni, H., Yang, R., et al.: Intelligent virtual reference feedback tuning and its application to heat treatment electric furnace control. Eng. Appl. Artif. Intell. 46, 1–9 (2015)CrossRefGoogle Scholar
  9. 9.
    Guardabassi, G., Savaresi, S.M.: Virtual reference direct design method: an off-line approach to data-based control system design. IEEE Trans. Autom. Control 45(5), 954–959 (2000)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Formentin, S., Campi, M.C., Savaresi, S.M.: Virtual reference feedback tuning for industrial PID controllers. IFAC Proc. Vols. 47(3), 11275–11280 (2014)CrossRefGoogle Scholar
  11. 11.
    Campi, M.C., Lecchini, A., Savaresi, S.M.: Virtual reference feedback tuning: a direct method for the design of feedback controllers. Automatica 38(8), 1337–1346 (2002)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wang, L., Ni, H., Yang, R., Fei, M., Ye, W.: A simple human learning optimization algorithm. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds.) LSMS/ICSEE 2014, Part II. CCIS, vol. 462, pp. 56–65. Springer, Heidelberg (2014). Scholar
  13. 13.
    Wang, L., Yang, R., Ni, H., et al.: A human learning optimization algorithm and its application to multi-dimensional knapsack problems. Appl. Soft Comput. 34, 736–743 (2015)CrossRefGoogle Scholar
  14. 14.
    Li, X., Yao, J., Wang, L., Menhas, M.I.: Application of human learning optimization algorithm for production scheduling optimization. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds.) LSMS/ICSEE 2017, Part I. CCIS, vol. 761, pp. 242–252. Springer, Singapore (2017). Scholar
  15. 15.
    Wang, L., Pei, J., et al.: A hybrid-coded human learning optimization for mixed-variable optimization problems. Knowl. Based Syst. 127, 114–125 (2017)CrossRefGoogle Scholar
  16. 16.
    Li, C., Ye, J., Zhao, M.: Two-degree-of-freedom model driving control of evaporator water level in nuclear power plant. Yunnan Chem. Technol. 43(5), 55–60 (2016)Google Scholar
  17. 17.
    Åström, K.J., Hägglund, T.: Revisiting the Ziegler-Nichols step response method for PID control. J. Process Control 14(6), 635–650 (2004)CrossRefGoogle Scholar
  18. 18.
    Wang, L., Yang, R., Pardalos, P.M., et al.: An adaptive fuzzy controller based on harmony search and its application to power plant control. Int. J. Electr. Power Energy Syst. 53, 272–278 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Zhi Han
    • 1
  • Hu Qi
    • 2
  • Ling Wang
    • 1
    Email author
  • Muhammad Ilyas Menhas
    • 1
    • 3
  • Minrui Fei
    • 1
  1. 1.Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina
  2. 2.Shanghai Power Construction Testing InstituteShanghaiChina
  3. 3.Department of Electrical EngineeringMirpur University of Science and TechnologyMirpur A.K.Pakistan

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