This paper studies the distributed data-driven event-triggered model free adaptive iterative learning control (ETMFAILC) of multiple high-speed trains (MHSTs) under iteration-varying topologies, which breaks away from the dependence on the train dynamics. Firstly, the nonlinear MHSTs with unknown dynamics are converted into a linear model. Then, combining the proposed event-based triggering condition and the linear model, the ETMFAILC scheme under the fixed topology is designed. Next, theoretical analysis proves the bounded input bounded output (BIBO) stability of MHSTs. Finally, the study is extended to the switching topologies and the validity of the ETMFAILC is verified by a numerical example.
Distributed Event-Triggered Iterative Learning Control for Multiple High-Speed Trains With Switching Topologies: A Data-Driven Approach
IEEE Transactions on Intelligent Transportation Systems ; 24 , 10 ; 10818-10829
2023-10-01
8450896 byte
Article (Journal)
Electronic Resource
English
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