Open AccessIterative learning and adaptive fault‐tolerant control with application to high‐speed trains under unknown speed delays and control input saturations
Author(s) -
Fan Lingling
Publication year - 2014
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0498
Subject(s) - control theory (sociology) , train , electronic speed control , iterative learning control , computer science , fault tolerance , adaptive control , control (management) , control engineering , speedup , engineering , artificial intelligence , distributed computing , cartography , electrical engineering , geography , operating system
This study investigates the speed trajectory tracking problem of high‐speed trains with actuator failures and unknown speed delays as well as control input saturations. New adaptive iterative learning fault‐tolerant control (AILFTC) strategy is derived without the need for precise system parameters or analytically estimating bound on actuator failures variables. It is shown that with the proposed method, both actuator failures can be accommodated and the unknown time‐varying speed delays and control input saturations can be analysed by means of Lyapunov–Krasovskii function. As such, the resultant control algorithms are able to achieve the L [0, T ] 2 convergence of the train speed to desired profile during operations repeatedly in the presence of non‐linearities and parametric uncertainties, as validated by the theoretical analysis and numerical simulations.