Improved Model Predictive Control Path Tracking Strategy Based an Online Updating Algorithm With Cosine Similarity and a Horizon Factor

Path tracking plays an essential role in autonomous vehicles. To ensure tracking accuracy and improve tracking adaptability in different velocities, a path tracking strategy based on an improved model predictive control (MPC) method is presented in this research. First, a path tracking controller based on improved MPC based an online Updating algorithm is constructed. The update mechanism is triggered by using the cosine similarity, when the cosine similarity is lower than the predefined threshold value, making the state space and cost function of MPC match real-time conditions to rectify the sensitivity of MPC to vehicle speed. Additionally, to further enhance the controller’s performance, a fuzzy control is employed to determine the horizon factor for optimizing the prediction horizon and control horizon online. Also, the weighting factors of the prediction horizon and control horizon are discussed to improve the adaptability at varying velocities. Next, the improved MPC controller is compared with the traditional MPC controller for a double lane change maneuver. The validation results demonstrate that the proposed strategy achieves good adaptability with satisfactory tracking accuracy at various velocities. Finally, the feasibility of the proposed strategy is verified in a real prototype vehicle test.