Physics-Constrained Dual-Attention Reinforcement Learning for Semi-Active Lateral Vibration Control of High-Speed Trains

High-speed trains are prone to severe lateral vibrations induced by track irregularity excitations under complex operating conditions, which deteriorate ride comfort, reduce running stability, and accelerate wheel–rail wear. Existing lateral suspension vibration control methods mainly rely on fixed parameters and expert experience. To further optimize and improve the control performance of semi-active lateral suspension systems for high-speed trains, this paper proposes the Physics-Constrained Dual-Attention Reinforcement Learning for Semi-Active Lateral Vibration Control of High-Speed Trains (SA-PRL). The proposed method is built upon a semi-active Twin Delayed Deep Deterministic Policy Gradient algorithm (SATD3). To ensure the physical realizability of control actions, the Logical Constraints of Skyhook Control (LCSC) are introduced to map the controller output into physically feasible damping commands. Furthermore, to enhance the perception of critical state features and improve value estimation capability, a Critic Network with Dual-Head Self-Attention (CDHA) is developed. Based on a railway vehicle lateral dynamic model, a series of simulation experiments are conducted under four excitation conditions, including single-peak sinusoidal excitation and multi-peak sinusoidal excitation, as well as the Chinese high-speed railway track irregularity (CHSRTI) and German low-interference track irregularity (GLITI) excitations. The results demonstrate the lateral vibration suppression capability of the proposed method over passive suspension, skyhook damping control, and existing reinforcement learning methods, with reductions of 47.5% and 47.7% in the RMS carbody lateral acceleration under the CHSRTI and GLITI spectra, respectively. The proposed method provides an effective solution for semi-active lateral vibration control of high-speed trains.