Fuzzy Observer-Based Transitional Path-Tracking Control for Autonomous Vehicles

This study addresses the path-tracking control issue of autonomous vehicles (AVs) when the GPS measurement is temporarily unavailable. In such a case, the vehicle states, location or the curvature of the reference path might be unobtainable, while the camera can be potentially used to detect the pathtracking states. To this end, this paper proposes a fuzzy-observerbased composite nonlinear feedback (CNF) controller with a Takagi-Sugeno (T-S) vehicle lateral dynamic model to guarantee the normal path-tracking maneuver and improve the transient performance. A parallel distributed compensation (PDC)-based CNF control method is developed to realize the control objective with the T-S vehicle model considering the transient performance and actuator saturation. The closed-loop stability and H-infinity index performance integrating the tracking and estimation errors have been proved with a Lyapunov approach. The observer-based controller design has been implemented based on the formulation of the linear matrix inequalities (LMIs). High-fidelity simulations using CarSim-Matlab/Simulink have demonstrated the validity of the proposed approach in terms of enhancing the tracking performance under the input saturation and disturbances in GPS-denied environments.

Automated summary

This study proposes a fuzzy-observer-based control method using a Takagi-Sugeno vehicle dynamic model to address path-tracking control issues of autonomous vehicles when GPS is temporarily unavailable, demonstrating its effectiveness through high-fidelity simulations.