Abstract
Fuzzy control is one of the important branches of intelligent control. And its optimization problem has become more and more important. The knowledge base formed by the experience of the researcher can often determine the performance of the fuzzy controller. Therefore, the optimization of the fuzzy control is mainly the optimization of the knowledge base. This paper will focus on using the seeker optimization algorithm (SOA) to optimize the membership function in the fuzzy controller knowledge base. Through the Matlab simulation, the original fuzzy control is compared with the SOA fuzzy control. The results show that the control effect of SOA fuzzy control is better than that of original fuzzy control.
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References
Zhou W, Zhu J (2002) On-line optimization of fuzzy control factor by uniform design method. Ind Control Comput (04):31–34
Xu W, Fei M (2012) Design and implementation of circulating fluidized bed temperature control based on harmonic optimization fuzzy controller. Instrum Technol (10):30–32+35
Fang Q (2003) Optimal design of fuzzy control membership function. J Electron Meas Instrum (03):42–45
Xia C, Guo P, Shi T, Wang M (2005) Adaptive control of brushless DC motor based on fuzzy genetic algorithm. Proc CSEE (11):129–133
Zhang W, Lu Y (2003) Mobile robot navigation based on improved genetic algorithm and fuzzy logic control. Robot (01):1–6
Jin Y, Jiang J (1996) Optimization of fuzzy control rules based on evolutionary computation. Control Decis (06):672–676
Huang H, Li A, Lin X (2007) Fuzzy control method based on immune genetic algorithm optimization and its application. J Comput Appl (07):1737–1740+1750
Yu J (2008) Advanced Control Technology in Industrial Processes, vol 8, pp 232–239. East China University of Science and Technology Press, Shanghai
Yu S, Cao Z (2014) Parameter optimization of PID controller based on seeker optimization algorithm. Comput Simul 31(09), 347–350+373
Xuewei L, Lixin Ma, Yihu Y (2019) Research on fuzzy PID vector control based on fuzzy scaling factor optimization variable universe. Electr Power Sci Eng 35(02):14–19
Yannian W, Hao Z, Huan Y, Yanjie C, Wenting L (2018) Application of genetic algorithm optimization fuzzy PID controller in intelligent hydraulic servo control system. Foreign Electron Meas Technol 37(12):125–128
Kewang P, Xuyu L, Yayun S (2018) Fuzzy PID control of bridge crane based on particle swarm optimization. Meas Control Technol 37(10):127–131
Yanwei X, Donghui W (2018) Weight decision grey wolf optimization algorithm based on fuzzy control. Comput Syst 27(10):202–208
Wei C, Qiao W (2018) Research on motion control of crawler robot based on particle swarm optimization fuzzy PID. Mod Electron Technol 41(18):49–53
Acknowledgments
This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. SJCX 18_0906) and the Natural Science Foundation of Huaiyin Institute of Technology (Grant No. 18HGZ002).
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Hua, R., Zhao, H. (2020). Optimization and Simulation of Fuzzy Control Based on SOA. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_32
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DOI: https://doi.org/10.1007/978-981-32-9698-5_32
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