Parameter Identification of Train Braking System Based on Gauss-Newton Iteration Method

This paper presents an identification algorithm with Gauss-Newton iteration method for train braking system parameter estimate. A nonlinear system model with parametric uncertainties is built through the analysis of the flow process of the train braking signal as the command and the characteristics of the braking system. Gauss-Newton method is employed to improve the convergence speed of the identification algorithm, which can reduce calculation time. In the end, the simulation analysis is conducted based on the actual data of Beijing-Guangzhou Railway. Simulations are presented to demonstrate the effectiveness of the proposed identification algorithm, which has anti-interference performance to deal with the external noise disturbance and estimates the braking system parameters quickly and accurately.