Model Free Adaptive Iterative Learning Fault-tolerant Control for High-speed Trains with Speed and Input Constraints

In this work, a model free adaptive iterative learning fault-tolerant control (MFAILFTC) strategy is proposed to address the speed trajectory tracking problem of high-speed trains (HSTs) with actuator failures as well as speed and traction/braking force constraints. Firstly, an equivalent compact form dynamic linearization (CFDL) data model of the HSTs with actuator failures is derived, and then the RBF neural network (RBFNN) is introduced to approximate fault function. Secondly, the modularized controller design with feedforward iterative learning control(ILC) added on the feedback model free adaptive control (MFAC) is proposed, which makes use of the periodicity of the high-speed trains effectively and improves the control performance greatly. Finally, in order to verify the effectiveness of the proposed strategy, simulation results are presented.