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In order to solve the problems of low integration, low reliability, and high cost caused by mechanical sensors used in bearingless synchronous reluctance motor (PMa-BSynRM) control system, a novel displacement self-sensing control method using a least square support vector machine (LSSVM) left inverse system is proposed. First, the working principle of the PMa-BSynRM is introduced and the mathematical model of the PMa-BSynRM is derived. Second, the observation principle of the left-inverse system of the PMa-BSynRM is explained and the left-invertibility of the displacement subsystem is proved. Thirdly, the improved NSGA-II algorithm is utilized to optimize the regularization parameter and the bandwidth of LSSVM, and the displacement self-sensing control system is constructed. The simulations of speed variation and anti-interference are performed, which proves the dynamic tracking performance of the displacement. Finally, the static suspension, speed variation and anti-interference experiments are carried out. The feasibility and reliability of the proposed displacement self-sensing method are verified.

In this study, an improved centripetal force type-magnetic bearing (CFT-MB) for a flywheel battery system is proposed, which is easy to process and it has better performance with superior stiffness and anti-interference characteristics than that of the pure spherical CFT-MB. First, the configuration, magnetic circuits, working principle and mathematical model of the improved CFT-MB are analyzed in detail. The electromagnetic characteristics are then analyzed. In comparison with the analysis results of the pure spherical CFT-MB, the improved CFT-MB has less cost, less force–displacement and force–deflection stiffness, and higher force-current stiffness, which verify its preferable design concept. Related comparative experiments based on the prototype are also conducted. The stiffness tests results verify the accuracy of the previously presented electromagnetic characteristic analysis results. Performance tests results show that the proposed improved CFT-MB is superior to that of the original CFT-MB in resisting to the disturbance or the gyroscopic effect, which verifies the accuracy of the theoretical analysis.

In order to realize the strong antidisturbance capability and the precise control of suspension forces in a bearingless permanent magnet slice motor (BPMSM), an active disturbance rejection control (ADRC) strategy based on the combination of resilient backpropagation neural network (RPROPNN) and improved differential evolution (IDE) algorithm is proposed. Based on the mathematical model of the BPMSM, the first-order ADRC controller of the rotating part and the second-order ADRC controller of the suspension part are designed, respectively, for the BPMSM according to the different orders of the BPMSM system. Then, the elite group bootstrap mechanism and parameter adaptive techniques are introduced to improve the DE algorithm to speed up its convergence and enhance the global search capability, and the initial weights of RPROPNN are optimized using the IDE algorithm to obtain the best network model. According to the feedback information of the BPMSM, the optimization mechanism and self-learning capability of RPROPNN are used to adjust the parameters of ADRC to reduce the dependence of ADRC on parameters. The comparative experimental results indicate that the proposed ADRC optimization design method has stronger robustness when disturbances occur, and the method is feasible and effective.

Aiming at the defects of signal detection and transmission delay leading to the reduction of control accuracy in the digital control of bearingless permanent magnet slice motor (BPMSM), a three-vector model predictive suspension force control (MPSFC) strategy is proposed. First, the suspension force prediction model of the BPMSM is established and the dead-beat principle is used to calculate two active voltage vectors and a null vector duty cycle. Then, according to the principle of the minimum switching state in one control cycle, simplifying the candidate voltage vector combinations, which effectively reduces the calculation amount and inverter switching frequency. Finally, the simulation and experimental results show that the proposed three-vector MPSFC strategy of the BPMSM can not only effectively improve the control accuracy, but also implement the stable suspension of the rotor. In addition, it has good static and dynamic response characteristics.

In order to solve the problem that the unbalance vibration caused by rotor mass eccentricity of the four-degree-of-freedom (four-DOF) six-pole hybrid magnetic bearings (HMBs), a feed-forward compensation control strategy based on variable step size least-mean-square (LMS) adaptive filter is proposed. First, the structure, operation principle and mathematical model of the four-DOF six-pole HMBs are introduced, the motion law of rotor vibration is analyzed, and the dynamic equation is derived. Second, the variable step size LMS adaptive filter is improved by using the Versoria function. The step size of LMS algorithm is adjusted adaptively by using Versoria function model and Takagi–Sugeno (TS) fuzzy inference machine. The LMS adaptive filter can generate a signal with the same frequency, amplitude, and phase as the rotating speed to offset the vibration signal, so as to realize the vibration compensation control of the four-DOF six-pole HMB rotor. Finally, the simulations and experiments not only prove the effectiveness of this method but also show that the method has good compensation accuracy. The result shows that the Versoria function used in variable step size LMS adaptive filter for vibration compensation is better than the traditional variable step size filtering algorithm based on Sigmoid function.