Research on Adaptive Predictive Control Algorithm for Autonomous Driving

Autonomous vehicles hold significant promise for enhancing traffic safety, alleviating urban congestion, and reducing energy consumption. This study proposes an adaptive Model Predictive Control (MPC)-based trajectory tracking controller to address the issue of increased tracking errors caused by vehicle kinematic parameter variations due to changing speeds in autonomous driving. The controller employs polynomial fitting to adaptively adjust control parameters within the prediction horizon based on varying vehicle speeds, ensuring high-precision trajectory tracking in diverse and complex environments. Multi-scenario simulation results on the Carsim, MATLAB, Simulink co-simulation platform demonstrate that the proposed controller effectively improves control performance under different speeds and prediction horizons. Compared to traditional MPC methods, the controller exhibits enhanced stability and accuracy in complex driving scenarios. The experimental results validate the effectiveness of the proposed approach, providing a novel perspective for adaptive control strategies in the field of autonomous driving. Furthermore, this research investigates the impact of varying prediction horizons on controller performance, specifically analyzing the trade-off between tracking accuracy and computational load. The adaptive strategy demonstrates a capability to dynamically adjust the prediction horizon in response to real-time driving conditions, ensuring both robust tracking and efficient computational performance. By minimizing lateral and heading errors, the proposed controller enhances overall vehicle stability and responsiveness. This study contributes to the advancement of autonomous driving by offering a practical and adaptable control solution for trajectory tracking.