@article{Alhazmi2025, 
author = {Muflih Alhazmi and Waqar Ul Hassan and Mohammed M. A. Almazah and Saadia Rehman and Azmat Ullah Khan Niazi and Somayya Komal and Nafisa A. Albasheir},
title = {Fault-tolerant coordination of robotic car teams via adaptive neural control and real-time fault isolation},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {8},
pages = {19554-19585},
keywords = {robotic car systems, fault-tolerant control, dynamic adjustment neural network (DANN), sensor and actuator faults, cooperative control, fault detection and isolation (FDI), Lyapunov stability, adaptive neural control, formation control, autonomous driving systems},
url = {https://www.sciopen.com/article/10.3934/math.2025873},
doi = {10.3934/math.2025873},
abstract = {This paper investigates robust cooperative control strategies for multi-robotic car systems operating under sensor and actuator faults. In autonomous driving environments, the degradation or failure of sensors and actuators significantly affects the performance of the system, posing risks to formation control, velocity tracking, and safety. To address these challenges, we propose a robust neural control framework that integrates a dynamic adjustment neural network (DANN) with fault-tolerant design. This architecture enables each robotic car to adaptively learn the system dynamics and adjust control signals in real time, even in the presence of component faults. A fault detection and isolation (FDI) mechanism is incorporated to identify malfunctioning elements, allowing the control system to dynamically compensate and maintain coordinated behavior. Lyapunov-based analysis is employed to guarantee stability and convergence of the system. In addition to theoretical development, a detailed simulation example involving a team of robotic cars under various sensor and actuator fault scenarios is presented to demonstrate the effectiveness and robustness of the proposed control strategy. The results confirm reliable tracking performance, strong resilience, and improved formation stability under realistic fault conditions.}
}