Adaptive event-triggered fuzzy control for autonomous vehicle path tracking with steer-by-wire systems

The rapid advancement of autonomous vehicle technology has raised stringent requirements on the accuracy and robustness of vehicle path tracking control systems. However, the nonlinearities of vehicle dynamics, system uncertainties, and bandwidth limitations of in-vehicle networks pose significant challenges to the design of high-performance controllers. Existing hierarchical control methods often treat path tracking calculation and steering actuator execution as independent modules, typically assuming that the lower layer can perfectly execute the steering commands from the upper layer. Moreover, the growing number of onboard sensors challenges the data transmission capacity of the in-vehicle communication network. Conventional adaptive event-triggered (AET) schemes suffer from monotonically evolving thresholds, which eventually causes them to degrade into static event-triggered (SET) strategies and lose adaptive capacity. To address these issues, this paper proposes an integrated path tracking control strategy for autonomous vehicles based on a steer-by-wire (SbW) system, incorporating a nonmonotonic adaptive event-triggered (NAET) mechanism. The proposed strategy unifies path tracking control and steering actuator dynamics into a constrained optimization problem, and the NAET mechanism maintains adaptive threshold adjustment without degrading into an SET strategy. A robust fuzzy dynamic output-feedback (DOF) controller is designed to improve tracking accuracy, reduce reliance on sensors, and conserve in-vehicle network resources. Hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed method. The results demonstrate that the integrated framework achieves superior path tracking performance, while the NAET scheme significantly reduces redundant data transmissions over the in-vehicle network.