Fuzzy modeling and robust control based on intelligent feature extraction method

被引：0
|
作者：
Xu Y. [1 ]
Pan M. [1 ]
Huang J. [1 ]
机构：
[1] College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Hangkong Dongli Xuebao/Journal of Aerospace Power | 2021年 / 36卷 / 07期
关键词：
Affinity propagation algorithm; Characteristic parameters; Full-envelope T-S fuzzy state space model; Reference model H[!sub]∞[!/sub] controller; T-S (Takagi-Sugeno) fuzzy model;
D O I：
10.13224/j.cnki.jasp.20210802
中图分类号：
学科分类号：
摘要：
Considering the difficulty of establishing the aero-engine linear model in the full envelope online, a fuzzy modeling method based on intelligent feature extraction was proposed. The characteristic parameters of aero-engines were designed, the aero-engine characteristics were extracted by the affinity propagation algorithm, and the steady-state operating point at the center of the characteristic extraction region was taken as nominal point of the Takagi-Sugeno (T-S) fuzzy model to obtain the full-envelope T-S fuzzy state space model. Combining the T-S fuzzy model with H∞ control theory, the reference model H∞ controller was designed and simulated. The simulation results showed that the steady state error of the T-S fuzzy state space model was in the order of 10-3, which met the requirement of model accuracy, and the H∞ controller had an excellent control quality such that the response time was less than 1s and there was no steady-state error. © 2021, Editorial Department of Journal of Aerospace Power. All right reserved.
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页码：1545 / 1555
页数：10
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共 24 条
  • [1] SUGIYAMA N., Derivation of system matrices from nonlinear dynamic simulation of jet engines, Journal of Guidance, Control, and Dynamics, 17, 6, pp. 1320-1326, (1992)
  • [2] FENG Zhengping, SUN Jianguo, HUANG Jinquan, Et al., A new method for establishing a state variable model of aeroengine, Journal of Aerospace Power, 13, 4, pp. 86-89, (1998)
  • [3] JIA Xinchun, ZHANG Dawei, ZHENG Lihong, Et al., Modeling and stabilization for a class of nonlinear networked control systems:a T-S fuzzy approach[J], Progress in Natural Science, 18, 8, pp. 1031-1037, (2008)
  • [4] ZHANG Hong, FANG Huajing, ZHU Quanmin, Modelling and stability analysis of nonlinear networked control systems based on quasi T-S fuzzy model[J], International Journal of Modelling, Identification and Control, 24, 1, pp. 42-51, (2015)
  • [5] RASTEGAR S, ARAUJO R, MENDES J., A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems, Journal of Vibration and Control, 22, 7, pp. 1820-1837, (2016)
  • [6] CHEN Minglai, LI Junmin, Modeling and control of T-S fuzzy hyperbolic model for a class of nonlinear systems, Proceedings of International Conference on Modelling, Identification and Control, pp. 57-62, (2012)
  • [7] WANG Hongwei, MA Guangfu, WANG Zicai, Fast algorithm of on-line fuzzy modeling for nonlinear systems, Chinese Journal of Aeronautics, 20, 3, pp. 48-50, (1999)
  • [8] JIANG Qingfeng, ZENG Wenjie, Application of T-S fuzzy switching controller in core power control, Nuclear Power Engineering, 41, 6, pp. 126-130, (2020)
  • [9] SHENG Chunyang, ZHAO Jun, WANG Wei, Et al., A fuzzy method based on T-S model for blast furnace gas system, Journal of Shanghai Jiaotong University, 46, 12, pp. 1907-1913, (2012)
  • [10] LIAN Honghai, DENG Peng, XIAO Shenping, Et al., Robust sampled-data control for nonlinear systems based on T-S fuzzy model, Control Theory and Applications, 37, 7, pp. 1601-1610, (2020)
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