Fault-Tolerant Control Strategies for High-Speed Train Systems

High-speed train systems demand exceptionally reliable control schemes to maintain safety and performance in the face of actuator malfunctions, sensor degradations, communication delays and unpredictable external disturbances such as crosswinds. Fault-tolerant control (FTC) strategies for these systems broadly fall into passive and active categories. Passive FTC designs seek inherent robustness, relying on conservative margins and robust control laws to accommodate faults without reconfiguration. Active FTC methods incorporate fault detection and diagnosis modules to identify anomalies and reconfigure controllers in real time, often blending model-based observers with adaptive mechanisms. More recent progressive or hybrid approaches fuse passive robustness and active reconfiguration, gradually refining controller parameters as fault information accumulates. Core methodologies include sliding mode control to ensure finite-time convergence and reject matched disturbances, predictive control to compensate for network-induced delays, adaptive estimation of model uncertainties, and neural-network-based observers for fault estimation. Distributed and cooperative architectures extend single-train schemes to multi-train formations, enabling decentralised reconfiguration under moving-block signalling. Across all approaches, the twin imperatives of rapid fault accommodation and minimal impact on ride comfort drive ongoing innovation. By integrating advanced optimisation techniques and learning-based predictors, modern FTC frameworks promise safer, more resilient high-speed rail operations worldwide.

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Fault-Tolerant Control Strategies for High-Speed Train Systems publication trend

The graph below shows the total number of articles in fault-tolerant control strategies for high-speed train systems across all publications each year (not limited to Nature Index journals).

Technical terms

Fault-tolerant control (FTC): A control paradigm designed to maintain acceptable system performance despite the occurrence of component faults or unexpected disturbances.

Sliding mode control (SMC): A robust control technique that drives system trajectories onto a predefined sliding surface and maintains them there, offering finite-time convergence and disturbance rejection.

Adaptive control: A real-time adjustment methodology that updates controller parameters based on observed system behaviour to accommodate unknown or time-varying uncertainties.

Actuator saturation: The physical limit on control inputs beyond which actuators cannot produce additional force or torque, often requiring compensation strategies to avoid performance degradation.

Radial basis function neural network (RBFNN): A feedforward neural architecture using radial basis functions as activation units, frequently employed for function approximation and fault estimation.

Actor–critic neural network: A reinforcement-learning structure comprising an “actor” that proposes control actions and a “critic” that evaluates them, enabling adaptive tuning under uncertainty.

References

1. Adaptive Fault-Tolerant Sliding Mode Control for High-Speed Trains With Actuator Faults Under Strong Winds. IEEE Access (2020).
2. Model-Independent Adaptive Fault-Tolerant Tracking Control for High-Speed Trains with Actuator Saturation. Applied Sciences (2019).
3. Distributed Cooperative Sliding Mode Fault‐Tolerant Control for Multiple High‐Speed Trains Based on Actor‐Critic Neural Network. Journal of Mathematics (2021).
4. Progressive Optimal Fault-Tolerant Control Combining Active and Passive Control Manners. Actuators (2024).
5. Improved Generalized Predictive Control for High‐Speed Train Network Systems Based on EMD‐AQPSO‐LS‐SVM Time Delay Prediction Model. Mathematical Problems in Engineering (2020).