A dynamic modeling approach for six-degree-of-freedom control of maglev planar motors with reference trajectory tracking

• Published in: Scientific reports Vol. 15; no. 1; pp. 13922 - 17
• Main Authors: Hu, Qi-Yu, Lu, Ze-Qi, Zhang, Jian-hua, Ding, Hu, Chen, Li-Qun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.04.2025; Nature Portfolio
• Subjects: 639/166/987; 639/166/988; 639/705/1041; Dynamic modeling; Electromagnetic forces; Humanities and Social Sciences; Magnetic levitation; multidisciplinary; Planar motor; Science; Science (multidisciplinary); Trajectory tracking; Trajectory tracking; Planar motor; Electromagnetic forces; Magnetic levitation; Dynamic modeling
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-96742-9

Magnetic levitation planar motors (MLPMs) exhibit significant potential in high-precision positioning applications, however the 6-degree-of-freedom (6-DOF) control performance is inherently limited by complex nonlinear dynamics. This paper proposes a 6-DOF dynamic modeling methodology for maglev planar motors based on reference trajectory tracking. The proposed approach synergistically combines magnetic flux analytical linearization with electromagnetic coupling field decoupling, establishing a unified control framework that comprehensively addresses kinematic nonlinearities and full-DOF coupling effects. By employing harmonic spectral analysis and a dual-reference coordinate transformation architecture, the method enables precise analytical derivation of electromagnetic force/torque distributions within the operational workspace. Numerical simulations validate the improved modeling accuracy, demonstrating a 5.3-fold reduction in wrench prediction errors under large yaw rotations compared to conventional methods. Finally, an experimental rig of the planar motor is manufactured to validate the theoretical trajectory tracking method. The experimental results confirm the robustness of the methodology in achieving high-precision motion control and long-stroke trajectory tracking, offering valuable insights for bridging theoretical modeling and industrial implementation of maglev planar motor systems.