Abstract：The principle and its approximative linear modeling of magnetic bearing system are given. The linear controller based on linear control theory and simplified modeling is developed, which results in the performance degradation and limitation of magnetic bearing system. The method of modeling and control of magnetic bearing system using nonlinear recurrent neural networks is studied,and the learning algorithm combined with the practical application of the magnetic bearing system is also developed. The proposed method restrains the effects of nonlinearity and uncertainty on the performance of magnetic bearing system,and has the ability of robustness,which improve the system performance greatly.
周锐, 房建成, 祝世平, 申功勋. 磁浮支承系统的神经网络建模与控制[J]. 北京航空航天大学学报, 1999, 25(6): 724-727.
Zhou Rui, Fang Jiancheng, Zhu Shiping, Shen Gongxun. Modeling and Control of Magnetic Bearing System Using Neural Networks. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 1999, 25(6): 724-727.
1. Matsumura F, Fujita M, Oida C. Theory and experiment of bearing combing radial and thrust control. IEEE Trans on Magnetics, 1987,23(5):2581~2583
2. Narendra K S, Parthasarathy K. Identification and control of dynamical system using neural networks. IEEE Trans on Neural Networks, 1990,1(1):4~27
3. 徐立新. 人工神经网络理论及其在控制中的应用研究:[学位论文]. 哈尔滨:哈尔滨工业大学自动控制系,1996
4. Fittro R L, Anand D K. Neural network controller design for a magnetic bearing flywheel energy storage system. N929047,1992
5. Wang Y N, Tong T S. Intelligent control integrating expert system and neural network. Advance in Model & Analysis, 1994,43(4):23~28
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