Machine Learning in Maglev Transportation Systems: Review and Prospects

Magnetic levitation (Maglev) technology has long garnered significant attention in the engineering community due to its inherent advantages, such as contactless operation, minimal friction losses, low noise, and high precision. Based on electromagnetic suspension (EMS) and electrodynamic principles, these systems are primarily developed for advanced transportation, while also inspiring emerging applications such as vibration isolation and flywheel energy storage. Despite progress, practical deployment faces critical challenges, including accurate modeling, robustness against nonlinear and uncertain dynamics, and control stability under complex conditions. Artificial intelligence (AI), particularly machine learning (ML) offers promising solutions. Studies show ML-based methods, i.e., improved particle swarm optimization (PSO) optimize proportional-integral-derivative (PID) to reduce controller output overshoot, deep reinforcement learning (DRL) reduces levitation gap fluctuation under complex conditions, ensemble learning achieves high fault diagnosis accuracy, and convolutional neural network-long short-term memory (CNN-LSTM) predictive maintenance cuts costs. This review summarizes recent AI-enabled advances in Maglev transportation system modeling, control, and optimization, highlighting representative algorithms, performance comparisons, technical challenges, and future directions toward intelligent, reliable, and energy-efficient transportation systems.