Work

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

Year: 2025

Type: article

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.

Source: Electronic Research Archive

DOI: 10.3934/era.2025174

Authors: Hui Zhao, Hanhong Cui, Yuan Zhao, Xuewu Dai

Institutions: Shenyang University of Technology, Dalian University, Northumbria University

Language: English

Topic: Railway Engineering and Dynamics

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,000