Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
