Optimization Method for Model Predictive Control Based on BiLSTM

Model predictive control (MPC) has been widely employed in autonomous driving control, offering advantages including rolling optimization. However, the performance of MPC can be significantly compromised due to the inaccurate system model of the vehicle. In response, a novel MPC model optimization algorithm based on BiLSTM has been proposed to address the issue of inaccurate system models. Firstly, MPC-based pathtracking data is collected under varying speeds, paths, and wheelbases. A BiLSTM network is then trained using this dataset and integrated with a MPC controller. This integration allows for the update of the wheelbase parameters of the kinematic model, thereby improving the accuracy of the vehicle prediction model. The proposed method is validated through simulation experiments. The results demonstrate that the path tracking accuracy of the MPC combined with a BiLSTM method is higher than that of MPC and linear quadratic regulator (LQR). Concurrently, this approach yields a considerable enhancement in path tracking precision, with an improvement of 37.53%.