Work

Decoupling control of bearingless brushless DC motor using particle swarm optimized neural network inverse system

Year: 2023

Type: article

Abstract: Neural network inverse system decoupling control can effectively solve the nonlinear strong coupling problem of bearingless brushless DC motor, but it has the problem of slow convergence and easy to fall into local extreme value. An Improved Particle Swarm Optimization (IPSO) algorithm has been employed to optimize the initial weights of the BP neural network inverse system. Comparisons between traditional inverse system decoupling control and the proposed method through simulations were conducted to confirm the efficacy and superiority of the proposed decoupling strategy. Furthermore, experiment research indicates that effective decoupling control can be achieved for both speed and radial displacement, as well as between the x and y-axis radial displacements, showcasing the good dynamic decoupling performance and stability of the proposed decoupling control strategy.

Source: Measurement Sensors

DOI: 10.1016/j.measen.2023.100952

Authors: Tao Tao, Lianghao Hua

Institution: Yangzhou Polytechnic Institute

Language: English

Topic: Magnetic Bearings and Levitation Dynamics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,800