Identifying the Magnetic Levitation System (33-210) through Neural Networks

Magnetic levitation systems, also known as Maglev, are electromechanical devices that employ electromagnetism to suspend ferromagnetic materials. Maglev systems have gained significant attention in recent years due to their ability to eliminate energy loss caused by friction, making them an attractive solution for various applications in the mobility fields such as rapid Maglev trains. However, This research focuses on the real-time identification of a Maglev model using a neural network. Real Experimental data from the 33-210 Maglev system is used to identify the system with shallow and deep neural networks. The shallow neural network produces a regression of 0.99986 and a performance of 9.2e-05 mean square error (MSE). Comparatively, the deep neural network exhibits superior results with a higher regression of 0.99987 and improved performance at 8.456e-05 (MSE).