Digital twin for magnetic levitation systems: General architecture design and uncertainty analysis

Digital twins (DTs) are widely used for actuator design, virtual prototyping, simulations, and analysis of model-based system engineering. DT technology is promising for magnetic levitation (maglev) systems, as illustrated by the mover design with high-strength neodymium magnets, the Lorentz force and torque (wrench) model comparison, and motion control verification. Digitalized maglev planar actuators (MLPAs) are time-, material-, labor-, and cost-efficient to develop, and the proposed DT is constructed using an open-source PyBullet module and assisted with a parallel-operated graphic user interface (GUI) using the PyQt5 module. Data transfer between physical systems and DTs is available using socket connections. After comparing the physical and virtual experimental results, the complete DT is verified using a 2-dimensional (2-D) Halbach array and single-disc magnet movers. The uncertainties of the MLPAs are implemented using white noise and system delay models. The ignored uncertainty features are introduced and analyzed for experimental deviations. The proposed DT provides a virtual safeguard environment for the next stage of machine learning research and multiple magnet-mover motion control studies. The first MLPA DT is established with a real-time wrench physics engine, which enables research opportunities for multi-mover motion, robotic collaboration, and artificial intelligence applications. This study is also beneficial for the design and analysis research of MLPAs.