Abstract
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.
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Acknowledgments
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).
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Han, Z., Qi, H., Wang, L., Menhas, M.I., Fei, M. (2018). Water Level Control of Nuclear Power Plant Steam Generator Based on Intelligent Virtual Reference Feedback Tuning. In: Li, K., Zhang, J., Chen, M., Yang, Z., Niu, Q. (eds) Advances in Green Energy Systems and Smart Grid. ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-13-2381-2_2
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DOI: https://doi.org/10.1007/978-981-13-2381-2_2
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