Search Results (2)

The application of active magnetic bearings (AMBs) in high-speed rotating machinery faces the challenge of micro-vibration. This research addresses the vibration control of a high-speed magnetically suspended turbo molecular pump (MSTMP) with rotor mass imbalance vibration and multi-stage-blade modal vibration. A novel integrated AMB controller consisting of parallel co-frequency adaptive notch filter (ANF) and cascaded multi-frequency improved double-T notch filters (DTNFs) is proposed. To suppress rotor mass imbalance vibration, a bandwidth factor rectification method of the ANF based on displacement stiffness perturbation is designed. To suppress multi-stage-blade modal vibration, a multi-objective constrained optimization method of cascaded improved DTNFs based on linear normalization is designed. Simulation and experimental results validate that the proposed structure improvement of the addition of an AMB controller and multi-parameter optimization of the algorithm can effectively improve not only the phase stability margin and the notch vibration performance of the magnetically suspended rotor (MSR) system but also the efficiency and practicability of the algorithm. At rotational speeds of 12,000 $\mathrm{rpm}$, 15,000 $\mathrm{rpm}$, 18,000 $\mathrm{rpm}$, and 21,000 $\mathrm{rpm}$, the suppression of co-frequency synchronous vibration is approximately maintained between −30.94 $\mathrm{dB}$ and −30.56 $\mathrm{dB}$. At the rated speed of 24,000 $\mathrm{rpm}$, compared with other algorithms, the value of the rotor displacement converges from 0.08 $\mathrm{mm}$ to 0.03 $\mathrm{mm}$, a reduction of 62.50%. The convergence time decreases from 3.67 $\mathrm{s}$ to 2.85 $\mathrm{s}$, a reduction of 22.34%. Full article

For a magnetically suspended control moment gyro (MSCMG), the high-speed rotor is actively suspended by magnetic bearings of 5-DOF, but the nonlinearity of the magnetic suspension force is one of the main reasons for the poor accuracy of radial translation control of the magnetically suspended rotor (MSR). To solve this problem, here, the characteristics of the magnetic suspension force are analyzed, and the nonlinear dynamic model of MSR is established. A sliding mode control (SMC) based on a neural network is presented, and the radial basis function (RBF) neural network is adopted to approximate the nonlinear displacement stiffness and the current displacement stiffness to weaken the chattering in SMC to improve the control accuracy of the MSR. The stability of the neural network SMC for the MSR is analyzed based on Lyapunov functions, and the rules of updating network weights are presented based on adaptive algorithms. Compared with these existing classic control methods, the simulation and experimental tests performed on a single-gimbal MSCMG with an angular momentum of 200 N.m.s indicated that this neural network SMC for MSR’s radial translation can not only make its suspension more stable but can also make its position precision higher. Full article