Abstract：To realize the dynamic decoupling control for the high-speed bearingless induction motor (BIM), an independent robust control method for the levitation subsystem is proposed based on the radial basis function neural network (RBFNN). The parameter uncertainty and external disturbance of the BIM are identified by the RBFNN. And then the RBFNN robust controller is designed based on the HJI (Hamilton-Jacobi-Isaacs) theory to realize the dynamic decoupling and independent control for the levitation subsystem in order to improve its operation stability and disturbance attenuation performance. The simulation and experiment results show the satisfied dynamic and static performances of the presented control system for the BIM.
孙宇新, 钱忠波. 无轴承异步电动机悬浮子系统独立鲁棒控制方法研究[J]. 电工技术学报, 2016, 31(7): 57-64.
Sun Yuxin, Qian Zhongbo. Study on Independent Robust Control of Levitation Subsystem for Bearingless Induction Motors. Transactions of China Electrotechnical Society, 2016, 31(7): 57-64.
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2016, Vol. 31 
