首页|Control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation

Control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
This paper presents a learning-based control policy design for point-to-point vehicle po-sitioning in the urban environment via BeiDou navigation.While navigating in urban canyons,the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou signal.Here,the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system,and the point-to-point localization con-trol is modeled and handled by leveraging off-policy reinforcement learning for feedback control.The proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system,without the full knowledge of the vehicle dynamics.It is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate.A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.

BeiDou navigationmultipath effectprescribed convergence ratereinforcement learningurban localization

QIN Yahang、ZHANG Chengye、CHEN Ci、XIE Shengli、LEWIS Frank L.

展开 >

School of Automation,Guangdong University of Technology,Guangzhou 510006,China

Guangdong Key Lab-oratory of IoT Information Technology,Guangzhou 510006,China

Center for Intelligent Batch Manufacturing Based on IoT Technology

Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing,Ministry of Education,Guangzhou 510006,China

Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing,Guangzhou 510006,China

Key Laboratory of Intelligent Information Processing and System Integration of IoT,Ministry of Education,Guangzhou 510006,China

UTA Research Institute,The University of Texas at Arlington,Fort Worth,TX 76019,USA

展开 >

国家自然科学基金国家自然科学基金Collaborative Innovation Center for Transportation Science and Technology of Guangzhou

6232010600862373114202206010056

2024

10.1007/s11424-024-3357-z

系统科学与复杂性学报(英文版)
中国科学院系统科学研究所

系统科学与复杂性学报(英文版)

EI
影响因子：0.181
ISSN：1009-6124
年,卷(期)：2024.37(1)
  • 42