Abstract
In order to solve the problems of the large volume and high cost of a six-pole hybrid magnetic bearing (SHMB) with displacement sensors, a displacement estimation method using a modified particle swarm optimization (MPSO) least-squares support vector machine (LS-SVM) is proposed. Firstly, the inertial weight of the MPSO is changed to achieve faster iterations, and the prediction model of an LS-SVM-based MPSO is built. Secondly, the prediction model is simulated and verified according to the parameters optimized by the MPSO, and the predicted values of MPSO and PSO are compared. Finally, static and dynamic suspension experiments and a disturbance experiment are carried out, which verify the robustness and stability of the displacement estimation method.
Highlights
General bearings are supported by a mechanical structure, which causes a large amount of friction between the stator and the rotor; many problems arise, such as considerable noise, a complex structure, high cost, and the failure of the stator–rotor connection due to mechanical friction [1]
A displacement estimation method using a modified particle swarm optimization least-squares support vector machine is proposed, which solves the problems of the large volume and high cost of six-pole hybrid magnetic bearings
The initial displacement prediction model of the magnetic bearings is established by taking the current parameter values as the performance values of least-squares support vector machine (LS-support vector machine (SVM)) when the iteration is 0
Summary
General bearings are supported by a mechanical structure, which causes a large amount of friction between the stator and the rotor; many problems arise, such as considerable noise, a complex structure, high cost, and the failure of the stator–rotor connection due to mechanical friction [1]. The neural network [19] method can be used to realize the self-sensing of rotor displacements This method does not depend on the model and parameters of the magnetic bearing. The high speed of training and convenient determination of model parameters bring new possibilities for accurately predicting rotor displacements [21,22]. In this manuscript, a displacement estimation method using a modified particle swarm optimization least-squares support vector machine is proposed, which solves the problems of the large volume and high cost of six-pole hybrid magnetic bearings. The particle swarm optimization under a linearly decreasing inertial weight falls into the local convergence. A rand dividual in the solution space is replaced with the particles obtained
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