Magnetic Field Modeling of Linear Halbach Array for Wallclimbing Robot Based on Radial Basis Function Neural Network

Abstract

Aiming at the problem that it is difficult to calculate the force of permanent magnets in the magnetic field, this paper proposes a nonlinear mechanical model of linear array magnetic field based on radial basis function neural network (RBFNN). Combined with the linear Halbach array adsorption module of the wall-climbing robot, the three-dimensional geometric magnetic fields of four typical linear array permanent magnets were constructed, and the theoretical models of the interaction between the magnetic fields were given respectively. Further, the finite element simulation calculation of the magnetic force was carried out using COMSOL Multiphysics. According to the parametric scanning results of the orthogonal test, a nonlinear intelligent prediction model of the force between magnetic fields with local loss sensitivity is established by using the RBFNN numerical fitting method. The average deviation of the network test set is 1.19, and the standard deviation is 0.80. The intelligent prediction model has strong generalization performance, faster convergence speed and stronger flexibility, which provides a theoretical basis for the interaction and control of array magnetic fields.