Data-driven feedforward compensation-based trajectory tracking control

Advanced autonomous driving is a critical component in the intelligent development of new-generation electric vehicles. Research on reliable chassis control algorithms ensures the safety and stability of autonomous vehicles during operation. To enhance the control performance of autonomous vehicles and improve the accuracy of trajectory tracking, this paper proposes a data-driven feedforward compensation trajectory tracking control approach. By optimizing the design of the feedforward compensation loop, systematic errors and latency in the vehicle’s steering system are mitigated, thereby enhancing the precision and robustness of the control algorithm. Initially, the paper analyzes the control errors present when the vehicle responds to controller commands. Subsequently, the paper focuses on the steering angle errors in trajectory tracking, identifying and analyzing the most relevant factors. A time-delay neural network (TDNN) based on data-driven principles is designed to model and predict these errors. This network captures the temporal characteristics of steering angle errors, enabling accurate predictions. Finally, the feedforward controller compensates for prediction errors by integrating feedforward compensation with the Model Predictive Control (MPC) controller’s predictions. This approach enables high-precision trajectory tracking by delivering precise control inputs. Experimental results demonstrate that the data-driven feedforward compensation control algorithm reduced trajectory tracking error by approximately 19% in co-simulations using Matlab/Simulink and Carsim, and by 56% in real-vehicle tests, thereby validating the effectiveness of the proposed approach.