A neural network inverse problem solution for a MIMO magnetic levitation actuator

• Published in: International journal of applied electromagnetics and mechanics Vol. 76; no. 1-2; pp. 197 - 204
• Main Authors: Zuidema, Gerlof, Krop, Dave C.J., Lomonova, Elena A.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.09.2024
• Subjects: inverse problem; neural network; permanent magnets; Magnetic levitation
• ISSN: 1383-5416; 1875-8800
• DOI: 10.3233/JAE-230221

A multiple-input and multiple-output planar actuator is proposed, which utilizes mechanically driven stator magnet arrays to levitate a permanent magnet mover. A state of levitation and actuation is obtained by mechanically altering the orientation of the stator magnets to control the forces and torques on the mover. A challenge for the design and control of the actuator is inverting the relationship between the force and stator magnet rotation angles, as there is no closed-form analytical solution. In this study, a feed-forward neural network is applied to model the forward relation between stator magnet angle input and a force and torque output to reduce the forward computation time for the design process and for error estimation in real-time applications. Additionally, the neural network is considered for inverting the solution for a motion profile sampled at 1000 Hz. The developed forward model is able to calculate the forces and torques on the mover a factor 10 faster than the equivalent charge or Fourier model with an absolute error of 3 mN and 0.1 mNm for the forces and torques, respectively, and a feed-forward neural network is able to accurately learn an inverse solution for small motion profiles.