EDS Data-Based Modeling and Experimental Verification

Recent advances in transportation technologies highlight the demand for faster and more efficient systems. Magnetic levitation has emerged as a key enabler in high-speed concepts such as the Hyperloop by eliminating mechanical contact, reducing friction, and improving stability. Among the different approaches, electrodynamic suspension (EDS) with Halbach array permanent magnets offers a promising solution for contactless high-speed travel, yet accurately modeling its nonlinear levitation dynamics remains challenging. This study compares a physics-based analytical model with a data-driven fuzzy logic model for a rotating Halbach array EDS system. An analytical lift-force expression is derived from electromagnetic principles and validated via three-dimensional (3D) finite-element simulations and static experiments, and a dynamic model is formulated to relate levitation gap to rotor speed. In parallel, an adaptive neuro-fuzzy inference system (ANFIS) is trained on experimental input–output data. Experimental evaluation on an independent test record shows that the fuzzy model achieves a lower root mean square error (RMSE) than the analytical model (0.0298 mm vs. 0.1064 mm). This gap reflects limits of analytical models using simplified assumptions (e.g., uniform magnetization and idealized conductivity), missing nonlinear, frequency-dependent effects. By learning directly from experimental measurements, the ANFIS model captures the aggregate influence of unmodeled nonlinearities and experimental effects within the investigated operating range. Consequently, the ANFIS-based model provides a more accurate representation of the measured levitation dynamics under the tested conditions, indicating its potential for real-time modeling and control applications in EDS systems.