Research on Automatic Train Driving Based on Fuzzy Proportion Integration Differentiation Iterative Control

Automatic train driving system is an important subsystem of train operation control system, which can provide passengers with punctual, accurate, efficient and fast transportation services. At the same time, the accurate stop, comfort and stability of the train is an important index to measure the control performance of the train automatic driving system, and the accurate stop plays a vital role in the efficient operation of the train. Based on the characteristics of high-speed train parking, an accurate parking algorithm based on fuzzy PID iterative control was proposed to solve the problem of low parking accuracy caused by frequent switching of control output. On the basis of solving the differential equation of the train braking model, the gradient of the system is obtained, and then the learning parameters of the convergence condition are obtained to overcome the repeated uncertainty in the stopping stage. The simulation results show that the fuzzy PID iterative control for asymptotic stability is an effective method to realize the precise parking of trains, and has strong robustness against the train parameter uncertainties and external disturbances.

Optimal Data-Driven Modeling-Free Differential-Inversion-Based Iterative Control: A Wafer Stage Example

In this paper, an optimal data-driven modeling-free differential-inversion-based iterative control (OMFDIIC) method is proposed for both high performance and robustness in the presence of random disturbances. Achieving high accuracy and fast convergence is challenging as the system dynamics behaviors vary due to the external uncertainties and the system bandwidth is limited. The aim of the proposed method is to compensate for the dynamics effect without modeling process and achieve both high accuracy and robust convergence, by extending the existed modeling-free differential-inversion-based iterative control (MFDIIC) method through a frequency- and iteration-dependent gain. The convergence of the OMFDIIC method is analyzed with random noise/disturbances considered. The developed method is applied to a wafer stage, and shows a significant improvement in the performance.