Decoupling Control of Six-Pole Hybrid Magnetic Bearing Based on LM Neural Network Inverse System Optimized by Improved Differential Evolution Algorithm

Abstract

In order to solve the problems of strong coupling and nonlinearity of the hybrid magnetic bearings for flywheel energy storage, a novel decoupling control combining Levenberg Marquardt neural network inverse system (LMNNIS) with improved differential evolution (IDE) algorithm is proposed for the six-pole radial-axial hybrid magnetic bearing (HMB). Firstly, the structure and principle of the HMB are introduced, and the mathematical model of its radial-axial suspension forces is deduced. The IDE algorithm is used to optimize the initial model parameters of LMNNIS, and the displacement and current data are collected and normalized to input into LMNNIS for training, so as to realize the decoupling control between the suspension forces of the HMB. Then, the simulation system is constructed, and the simulation experiments of the rotor floating and anti-interference are carried out, which show the convergence speed and anti-interference performance of IDE algorithm optimized LMNNIS are better than the traditional neural network inverse system. Finally, the verification experiments are carried out on the basis of the experimental platform, which prove the feasibility and reliability of the proposed method.