Open AccessOptimal Terminal Iterative Learning Control for the Parking Control System of Maglev Train
Author(s) -
Pengju Zhang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/587/1/012061
Subject(s) - maglev , iterative learning control , control theory (sociology) , process (computing) , convergence (economics) , braking distance , computer science , position (finance) , track (disk drive) , control (management) , terminal (telecommunication) , train , threshold braking , optimal control , line (geometry) , brake , control engineering , engineering , automotive engineering , mathematical optimization , mathematics , artificial intelligence , telecommunications , cartography , finance , economic growth , geography , electrical engineering , economics , operating system , geometry
This paper analyzes the parking process of maglev trains, establishes the corresponding mathematical model, based on the method of terminal iterative learning control(TILC), uses the stopping position error in the previous braking process to update the current control curve. In this paper, we select the initial braking position, initial speed or braking force or a combination thereof as the control input, and formulate the corresponding learning law. Finally, a line is simulated, and the traditional TILC method and the optimal TILC method are compared through simulation experiments, which verify that the latter has a faster convergence speed.