@article{Lei2025, 
author = {Siyu Lei and Chengye Zhang and Xiaobo Gu and Jingsong Qiu and Zheng Gao and Ci Chen},
title = {Learning-Based Robust Control Policy Design for Vehicular Navigation via Ultra-Wideband Communication},
year = {2025},
journal = {Unmanned Systems},
volume = {13},
number = {6},
pages = {1597-1612},
keywords = {Reinforcement learning, robust output regulation, point-to-point navigation, optional control, output tracking},
url = {https://www.sciopen.com/article/10.1142/S2301385025420038},
doi = {10.1142/S2301385025420038},
abstract = {In this paper, we investigate a learning-based framework that addresses the point-to-point vehicular navigation problem through a robust output regulation approach and test it through Ultra-Wide Band (UWB) communication noise data. Our focus is on the learning-based control policy design and ensuring the stability of the closed-loop system for vehicle dynamics. It is seen that our proposed solution, which relies on reinforcement learning, is data-driven and does not require accurate knowledge of the vehicle motion model. Specifically, we explore learning-based optimal tracking control through state feedback and output feedback, which we categorize as policy iteration and value iteration, respectively. To validate our approach, we incorporated noise samples from a real-world UWB positioning platform into our simulation. Our findings demonstrate that the learning-based method may be effective in both vehicular point-to-point tracking and interference tolerance, even in the presence of real-world noise interference.}
}