Embedded Machine-Vision Control of Electromagnetic Levitation Using Data-Driven Force Modelling

Electromagnetic levitation systems are instrumental in rail transportation, manufacturing machinery, contactless bearings, and precision scientific instrumentation. These systems are inherently open-loop-unstable, require accurate motion tracking to detect the position of the levitated object, and need effective real-time control to adjust the electromagnetic force. In this paper, we propose a machine-vision-based magnetic levitation system comprising a Raspberry Pi microcomputer programmed in Python, a machine-vision camera with software triggering, and a lifting electromagnet actuated using a model-based PID controller with compensation and feedforward augmentation. Since all components are available off-the-shelf, the present magnetic levitation system is cost-effective and easy to replicate. To the best of our knowledge, the present setup seems to be the first entirely based on a Raspberry Pi microcomputer; the first in which the electromagnetic force is predicted with an empirical correlation based on dedicated measurements, making analytical/numerical predictions unnecessary; and the first where the added mass is incorporated in the equation of motion of the levitated object (it is a non-negligible phenomenon, function of the position, for levitation in liquids). The visual tracking system has an accuracy of about ±15 micrometers, and the controller operates at 100 Hz. When actuating a sphere large enough to significantly distort the electromagnetic field, this magnetic levitation setup achieves position-tracking errors (RMS errors in the range of 74 to 194 μm during quasi-static and dynamic tests) comparable to or better than those of machine-vision-based magnetic levitation systems proposed in previous investigations, at a fraction of the cost.