Fault-tolerant coordination of robotic car teams via adaptive neural control and real-time fault isolation

Muflih Alhazmi; Waqar Ul Hassan; Mohammed M. A. Almazah; Saadia Rehman; Azmat Ullah Khan Niazi; Somayya Komal; Nafisa A. Albasheir; Muflih Alhazmi; Waqar Ul Hassan; Mohammed M. A. Almazah; Saadia Rehman; Azmat Ullah Khan Niazi; Somayya Komal; Nafisa A. Albasheir

doi:10.3934/math.2025873

AIMS Mathematics

2025, Volume 10, Issue 8: 19554-19585. doi: 10.3934/math.2025873

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Fault-tolerant coordination of robotic car teams via adaptive neural control and real-time fault isolation

1.
Department of Mathematics, College of Sciences, Northern Border University, Arar, Saudi Arabia; Muflih.Alhazmi@nbu.edu.sa
2.
Department of Mathematics, Faculty of Sciences, University of Mianwali, 42200, Mianwali, Punjab, Pakistan; waqarulhassan439@gmail.com; somayya.komal@umw.edu.pk
3.
Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil 61421, Saudi Arabia; mmalmazah@kku.edu.sa
4.
Department of Mathematics and Statistics, The University of Lahore, Sargodha 40100, Pakistan; saadiarehman468@gmail.com
5.
Department of Mathematics, College of Sciences and Arts (Magardah), King Khalid University, Magardah, 61421, Saudi Arabia; nalbasheir@kku.edu.sa

Received: 19 June 2025 Revised: 11 August 2025 Accepted: 14 August 2025 Published: 26 August 2025
MSC : 34H05, 93C10, 93D09

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.
Citation: Muflih Alhazmi, Waqar Ul Hassan, Mohammed M. A. Almazah, Saadia Rehman, Azmat Ullah Khan Niazi, Somayya Komal, Nafisa A. Albasheir. Fault-tolerant coordination of robotic car teams via adaptive neural control and real-time fault isolation[J]. AIMS Mathematics, 2025, 10(8): 19554-19585. doi: 10.3934/math.2025873

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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.

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