A neural network-based adaptive fault-tolerant cooperation control for multiple trains with unknown parameters

Hui Zhao; Hanhong Cui; Yuan Zhao; Xuewu Dai; Hui Zhao; Hanhong Cui; Yuan Zhao; Xuewu Dai

doi:10.3934/era.2025174

Electronic Research Archive

2025, Volume 33, Issue 6: 3931-3949. doi: 10.3934/era.2025174

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Research article

A neural network-based adaptive fault-tolerant cooperation control for multiple trains with unknown parameters

1.
School of Artificial Intelligence, Shenyang University of Technology, Shenyang 110870, China
2.
College of Information Engineering, Dalian University, Dalian 116622, China
3.
Department of Mathematics, Physics and Electrical Engineering, Northumbria University, NE1 8ST Newcastle upon Tyne, U.K

Received: 11 May 2025 Revised: 09 June 2025 Accepted: 17 June 2025 Published: 24 June 2025

For the problems of parametric uncertainties and actuator faults encountered during high-speed train operation, this paper proposes a neural network-based adaptive fault-tolerant control strategy for cooperation of multiple trains. First, a multi-train model with state constraints, disturbances, and actuator faults was established, and the radial basis function neural network was utilized to fit the unknown disturbance. Subsequently, the auxiliary control signal was introduced to compensate the impact of actuator faults on train. Then, when designing adaptive laws, the variations of basic resistance parameters were fully considered. By integrating adaptive laws with the adaptive fault-tolerant controller, the effect of disturbances on multi-train systems can be dynamically eliminated. The Lyapunov method was established to prove the stability of multi-train systems. Simulation results show that the proposed adaptive fault-tolerant control strategy can maintain the stability of multi-train systems and achieve cooperation of trains with actuator faults.
- high-speed trains,
- neural network,
- unknown parameters,
- actuator faults,
- adaptive fault-tolerant control
Citation: Hui Zhao, Hanhong Cui, Yuan Zhao, Xuewu Dai. A neural network-based adaptive fault-tolerant cooperation control for multiple trains with unknown parameters[J]. Electronic Research Archive, 2025, 33(6): 3931-3949. doi: 10.3934/era.2025174

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Abstract

For the problems of parametric uncertainties and actuator faults encountered during high-speed train operation, this paper proposes a neural network-based adaptive fault-tolerant control strategy for cooperation of multiple trains. First, a multi-train model with state constraints, disturbances, and actuator faults was established, and the radial basis function neural network was utilized to fit the unknown disturbance. Subsequently, the auxiliary control signal was introduced to compensate the impact of actuator faults on train. Then, when designing adaptive laws, the variations of basic resistance parameters were fully considered. By integrating adaptive laws with the adaptive fault-tolerant controller, the effect of disturbances on multi-train systems can be dynamically eliminated. The Lyapunov method was established to prove the stability of multi-train systems. Simulation results show that the proposed adaptive fault-tolerant control strategy can maintain the stability of multi-train systems and achieve cooperation of trains with actuator faults.

References

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