Output details

Coordinated Iterative Learning Control Schemes for Train Trajectory Tracking With Overspeed Protection

<22> This paper lays the theoretical foundation for a model-free control system that tunes control parameters with patterns extracted from data reflecting the complexity and the uncertainty of high-speed trains using machine learning. The paper proves the convergence of control parameters to the desired speed and position profiles along with learning, which ensures overspeed protection for high-speed trains. This system overcomes the problem of critical safety issues caused by the use of inaccurate models in control system design for normal trains. This research was supported by the National Natural Science Foundation of China and contributed to a further NNSF project.