Incorporating deep operator network into feedforward–feedback controller synthesis for path tracking of autonomous vehicles

Abstract

In this paper, a deep operator network (DeepONet)-based feedforward–feedback control framework for autonomous vehicles’ path tracking controller is introduced. In the proposed framework, DeepONet is utilized for data-driven vehicle dynamics modeling within the feedforward control component. To improve the accuracy of lateral dynamics, the physical model is integrated into the DeepONet architecture as prior knowledge. By incorporating experimentally validated physical models, the proposed approach captures the causal relationship between vehicle parameters (e.g. mass, tire-road friction) and vehicle dynamics, thereby enhancing the interpretability of the model. In the CarSim/Simulink simulation environment, the designed DeepONet-based feedforward–feedback controller is evaluated in two scenarios: double-lane change and oval track. The validation results demonstrate that the proposed approach achieves smaller lateral errors than other methods in both linear and nonlinear regions. Notably, when facing unknown roads and varying road friction conditions on the oval track, the DeepONet-based approach shows improvement in tracking accuracy compared to purely data-driven methods without physical model integration.