Model predictive control based on LSTM neural network for maglev vehicle’ suspension system

To improve the suspension performance of high-speed maglev vehicles under complex external disturbance, a composite model predictive control (MPC) algorithm based on a neural network is proposed. Firstly, the nonlinear dynamic response prediction model is constructed utilizing the long short-term memory (LSTM) neural network, and this model is trained by machine learning. Subsequently, a rolling optimization controller of the MPC algorithm is designed according to the vehicle suspension system’s prediction model and the suspension target. To compensate for the error of the prediction model resulting from changes in the control algorithm, a composite MPC algorithm is devised by combining both the proportional-integral-derivative (PID) algorithm and the MPC algorithm. This composite approach enables the suspension system to switch the selection of control algorithms in the suspension system according to the prediction error. Finally, the effectiveness of the composite MPC algorithm is verified by simulation and experiment. The results show that the prediction model based on the LSTM neural network can effectively predict the future dynamic response of the vehicle. Moreover, the proposed MPC algorithm can effectively suppress the suspension gap fluctuation in the high-speed maglev vehicle, thereby fostering improved stability in the suspension system.