Active suspension predictive control for high-speed trains based on disturbance feedforward and neural network residual compensation
In high-speed train active suspensions, unmodeled dynamics, wheel-rail wear, and track disturbances can cause dynamic mismatches that degrade the performance of model-dependent predictive control. This study proposes a composite model predictive control approach that integrates neural network residual compensation and equivalent track disturbance feedforward (NNRC-ETDF-MPC) to address this issue. The method firstly estimates the unknown equivalent track disturbances online using an augmented Kalman filter (AKF) and incorporated into the control framework as a physical feedforward compensation term. The nonlinear dynamic residuals are predicted and compensated online using a history-aware physical residual neural network (HA-PRNN), which uses a sliding window to extract multivariate coupling features. Based on these components, a dual feedforward composite control system is developed, along with a constraint-tightening mechanism to prevent actuator saturation. Finally, the performance of the proposed control strategy is evaluated using a co-simulation platform that includes two track spectra, a wide speed range, and cross-service wear conditions. Comparative tests show that the composite control strategy enhances the dynamic vibration suppression capabilities of high-speed trains working in complex, time-varying environments and enhancing robustness against model mismatch. Specifically, under worn wheel conditions, the proposed NNRC-ETDF-MPC achieves average Sperling index improvements of 22.81% and 23.48% across different track excitations.