The identification of axial magnetic bearing system based on LS-PSO algorithm

Abstract

System identification is an important basis of adaptive control and fault identification of active magnetic bearing (AMB) system. Among all the identification algorithms, least square method (LS) is a comparatively simple one with small calculation amount but a poor performance as well under colored noise, and particle swarm optimization algorithm (PSO) has a good performance under colored noise but a huge calculation amount. Taking advantage of the complementation of LS and PSO, the identification based on LS-PSO algorithm was proposed, which combines the briefness of LS and the high precision of PSO. In the novel algorithm, PSO updates the poles of identified transfer function model, and LS estimates the numerator optimally. The output error variance is minimized by repeating the procedure. In simulation, the calculation amount declined by 91.4% on average comparing with PSO, and the experiments validated that the error variance of LS-PSO declined by 92.92% relative to LS. The experimental results show that LS-PSO retains high precision of PSO under colored noise, while its calculation amount is greatly reduced by introducing LS, which is significant to practical application.