Model‐free adaptive fault‐tolerant control for subway trains with speed and traction/braking force constraints
Haojun Wang, Zhongsheng Hou
Abstract
This study investigates the subway train fault‐tolerant control problem for the actuator fault with constraints of speed and traction/braking force. The complex subway train dynamics is first transformed into a compact form dynamic linearization data model with the help of the concept pseudo‐partial derivative (PPD) proposed under the framework of model‐free adaptive control. By using the approximation of the uncertainty fault with radial basis function neural network (RBFNN), then a model‐free adaptive fault‐tolerant control (MFAFTC) scheme is designed by only using saturated input/output data of a subway train. The proposed MFAFTC scheme consists of two data driven on‐line learning updating algorithms for RBFNN‐weights and PPD, and its convergence is strictly proved by rigorous theoretical analysis, and whose correctness and effectiveness are further verified by a numerical simulation.