| Citation: | XIE Wenhai, XIE Zhenyu, XU Shaohan, et al. Fuzzy RBF neural network PID control strategy for non-directional magnetic bearing[J]. Journal of Aerospace Power, 2026, 41（3）：20240827 doi: 10.13224/j.cnki.jasp.20240827
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With introduction of non-directional differential control method, the bearing capacity of the radial magnetic bearing was improved by independently controlling each differential magnetic pole pair. As the conventional PID (proportion integration differentiation) control was found difficult to meet the control requirements of the complex nonlinear magnetic bearing system, the fuzzy RBF (radial basis function) neural network PID control strategy based on the non-directional differential control method was proposed to realize the online adjustment of PID parameters through an online training model so as to meet complex working conditions. Taking the 16-pole radial magnetic bearing as an example, the working principle of the conventional differential control method and the non-directional differential control method was analyzed, and the nominal maximum bearing capacity of the two control methods was compared. The theoretical calculation results showed that the nominal maximum bearing capacity of the radial magnetic bearing based on the non-directional differential control method increased by 30.66%. Simulink software was adopted to simulate the PID control strategy of the fuzzy RBF neural network. The simulation results showed that the control strategy had better static and dynamic performance. By setting up a test bench, actual maximum bearing capacity tests for radial magnetic bearing and high-speed rotation tests for the system were conducted. The test results showed that the actual maximum bearing capacity of the radial magnetic bearing based on the non-directional differential control method increased by 26.07%, and the fuzzy RBF neural network PID control strategy had better control effect compared with the conventional PID control strategy.
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