AUTHOR=Zhang Kun , Han Kezhen , Hu Zhijian , Tan Guoqiang 

TITLE=Privacy-preserving ADP for secure tracking control of AVRs against unreliable communication

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2025.1549414

DOI=10.3389/fnbot.2025.1549414

ISSN=1662-5218

ABSTRACT=In this study, we developed an encrypted guaranteed-cost tracking control scheme for autonomous vehicles or robots (AVRs), by using the adaptive dynamic programming technique. To construct the tracking dynamics under unreliable communication, the AVR's motion is analyzed. To mitigate information leakage and unauthorized access in vehicular network systems, an encrypted guaranteed-cost policy iteration algorithm is developed, incorporating encryption and decryption schemes between the vehicle and the cloud based on the tracking dynamics. Building on a simplified single-network framework, the Hamilton-Jacobi-Bellman equation is approximately solved, avoiding the complexity of dual-network structures and reducing the computational costs. The input-constrained issue is successfully handled using a non-quadratic value function. Furthermore, the approximate optimal control is verified to stabilize the tracking system. A case study involving an AVR system validates the effectiveness and practicality of the proposed algorithm.